UMass NLP | publications

This page lists most of our publications and preprints since 2015. (It includes a few non-paper citations, like invited talks or edited volumes.) Papers are also available from the personal websites of lab members.

2023

EACL

LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization

Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, and Kyle Lo

In European Chapter of the Association for Computational Linguistics, 2023
EACL Findings

ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, and Brendan O’Connor

In Findings of European Chapter of the Association for Computational Linguistics, 2023

2022

AAAI

An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity

Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, and Andrew McCallum

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
AAAI

Sublinear Time Approximation of Text Similarity Matrices

Archan Ray, Nicholas Monath, Andrew McCallum, and Cameron Musco

In Proceedings of the AAAI Conference on Artificial Intelligence, 2022
ACL

Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings

Shib Dasgupta, Michael Boratko, Siddhartha Mishra, Shriya Atmakuri, Dhruvesh Patel, Xiang Li, and Andrew McCallum

In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity can provide. Many relationships between words can be expressed set-theoretically, for example, adjective-noun compounds (eg. “red cars”⊆“cars”) and homographs (eg. “tongue”∩“body” should be similar to “mouth”, while “tongue”∩“language” should be similar to “dialect”) have natural set-theoretic interpretations. Box embeddings are a novel region-based representation which provide the capability to perform these set-theoretic operations. In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective. We demonstrate improved performance on various word similarity tasks, particularly on less common words, and perform a quantitative and qualitative analysis exploring the additional unique expressivity provided by Word2Box.
ACL

Event-Event Relation Extraction using Probabilistic Box Embedding

EunJeong Hwang, Jay-Yoon Lee, Tianyi Yang, Dhruvesh Patel, Dongxu Zhang, and Andrew McCallum

In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2022

To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a phone line “died” after “storm”, then it is obvious that the “storm” happened before the “died”. Current framework of event relation extraction do not guarantee this coherence and thus enforces it via constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. From our experiments, BERE also shows stronger conjunctive constraint satisfaction while performing on par or better in F1 compared to previous models with constraint injection.
ACL

Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions

Haw-Shiuan Chang and Andrew McCallum

In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

Neural language models (LMs) such as GPT-2 estimate the probability distribution over the next word by a softmax over the vocabulary. The softmax layer produces the distribution based on the dot products of a single hidden state and the embeddings of words in the vocabulary. However, we discover that this single hidden state cannot produce all probability distributions regardless of the LM size or training data size because the single hidden state embedding cannot be close to the embeddings of all the possible next words simultaneously when there are other interfering word embeddings between them. In this work, we demonstrate the importance of this limitation both theoretically and practically. Our work not only deepens our understanding of softmax bottleneck and mixture of softmax (MoS) but also inspires us to propose multi-facet softmax (MFS) to address the limitations of MoS. Extensive empirical analyses confirm our findings and show that against MoS, the proposed MFS achieves two-fold improvements in the perplexity of GPT-2 and BERT.
ACL

RELiC: Retrieving Evidence for Literary Claims

Katherine Thai, Yapei Chang, Kalpesh Krishna, and Mohit Iyyer

In Association of Computational Linguistics, 2022
EMNLP

RankGen: Improving Text Generation with Large Ranking Models

Kalpesh Krishna, Yapei Chang, John Wieting, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2022
EMNLP

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation

Tu Vu, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, and Noah Constant

In Empirical Methods in Natural Language Processing, 2022
EMNLP

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature

Katherine Thai, Marzena Karpinska, Kalpesh Krishna, William Ray, Moira Inghilleri, John Wieting, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2022
EMNLP

SLING: Sino Linguistic Evaluation of Large Language Models

Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2022
EMNLP

DEMETR: Diagnosing Evaluation Metrics for Translation

Marzena Karpinska, Nishant Raj, Katherine Thai, Yixiao Song, Ankita Gupta, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2022
EMNLP

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization

Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, and Andrew McCallum

In Empirical Methods in Natural Language Processing, 2022
EMNLP-Findings

You can’t pick your neighbors, or can you? When and How to Rely on Retrieval in the KNN-LM

Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum, Hamed Zamani, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2022
Field Matters

Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties

Tessa Masis, Anissa Neal, Lisa Green, and Brendan O’Connor

In Proceedings of the 1st Field Matters Workshop on NLP Applications to Field Linguistics, 2022
ICML

Knowledge base question answering by case-based reasoning over subgraphs

Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, and Andrew McCallum

In International Conference on Machine Learning, 2022
ICML

Interactive Correlation Clustering with Existential Cluster Constraints

Rico Angell, Nicholas Monath, Nishant Yadav, and Andrew McCallum

In International Conference on Machine Learning, 2022
NAACL

Entity Linking via Explicit Mention-Mention Coreference Modeling

Dhruv Agarwal, Rico Angell, Nicholas Monath, and Andrew McCallum

In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions. In this paper, we present and empirically analyze a novel training approach for learning mention and entity representations that is based on building minimum spanning arborescences (i.e., directed spanning trees) over mentions and entities across documents to explicitly model mention coreference relationships. We demonstrate the efficacy of our approach by showing significant improvements in both candidate generation recall and linking accuracy on the Zero-Shot Entity Linking dataset and MedMentions, the largest publicly available biomedical dataset. In addition, we show that our improvements in candidate generation yield higher quality re-ranking models downstream, setting a new SOTA result in linking accuracy on MedMentions. Finally, we demonstrate that our improved mention representations are also effective for the discovery of new entities via cross-document coreference.
NAACL

DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

Neha Kennard, Tim O’Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, and Andrew McCallum

In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors’ stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.
NAACL

Modeling Exemplification in Long-form Question Answering via Retrieval

Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, and Mohit Iyyer

In North American Association for Computational Linguistics, 2022
NAACL

ChapterBreak: A Challenge Dataset for Long-Range Language Models

Simeng Sun, Katherine Thai, and Mohit Iyyer

In North American Association for Computational Linguistics, 2022
NLP+CSS

Examining Political Rhetoric with Epistemic Stance Detection

Ankita Gupta, Su Lin Blodgett, Justin H Gross, and Brendan O’Connor

In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science, 2022
Negative Results

How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge?

Simeng Sun, Brian Dillon, and Mohit Iyyer

In Workshop on Insights from Negative Results in NLP @ ACL 2022, 2022
TIIS

ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive Investigation of Historical News Archives

Abram Handler, Narges Mahyar, and Brendan O’Connor

ACM Trans. Interact. Intell. Syst., 2022

Historians and archivists often find and analyze the occurrences of query words in newspaper archives to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual units, such as events, clusters, ranked documents, entity relationships, or thematic hierarchies. Informed by a study into the needs of historians and archivists, we thus propose ClioQuery, a text analytics system uniquely organized around the analysis of query words in context. ClioQuery applies text simplification techniques from natural language processing to help historians quickly and comprehensively gather and analyze all occurrences of a query word across an archive. It also pairs these new NLP methods with more traditional features like linked views and in-text highlighting to help engender trust in summarization techniques. We evaluate ClioQuery with two separate user studies, in which historians explain how ClioQuery’s novel text simplification features can help facilitate historical research. We also evaluate with a separate quantitative comparison study, which shows that ClioQuery helps crowdworkers find and remember historical information. Such results suggest possible new directions for text analytics in other query-oriented settings.
W-NUT

Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis

Chloe Eggleston and Brendan O’Connor

In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), 2022

2021

AAAI
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications

Haw-Shiuan Chang, Amol Agrawal, and Andrew McCallum

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021

Paper Bib Poster Slides
@inproceedings{chang2021extending, title = {Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications}, abbr = {AAAI}, bibtex_show = {true}, author = {Chang, Haw-Shiuan and Agrawal, Amol and McCallum, Andrew}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, year = {2021}, paper = {https://arxiv.org/abs/2103.15330}, poster = {https://f6d60bef-de96-4b94-b613-4913f88f2f0f.filesusr.com/ugd/e150d8_3d91e4f3cd6746aeaf24407fc0b674d1.pdf}, slides = {https://docs.google.com/presentation/d/1k-OBWdBYsGmXUuvNc1_J_JrEppB_Aqh82bJPCgjFL-s/edit?usp=sharing} }
ACL
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models.

Sumanta Bhattacharyya, Pedram Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum

In Association for Computational Linguistics, 2021

Paper Bib
@inproceedings{enmt21, abbr = {ACL}, bibtex_show = {true}, author = {Bhattacharyya, Sumanta and Rooshenas, Pedram and Naskar, Subhajit and Sun, Simeng and Iyyer, Mohit and McCallum, Andrew}, booktitle = {Association for Computational Linguistics}, year = {2021}, title = {Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models.}, paper = {https://aclanthology.org/2021.acl-long.349.pdf} }
ACL Findings

Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence

Andrew Halterman, Katherine Keith, Sheikh Sarwar, and Brendan O’Connor

In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021
Causal NLP

Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects

Katherine Keith, Douglas Rice, and Brendan O’Connor

In Proceedings of the First Workshop on Causal Inference and NLP, 2021

Using observed language to understand interpersonal interactions is important in high-stakes decision making. We propose a causal research design for observational (non-experimental) data to estimate the natural direct and indirect effects of social group signals (e.g. race or gender) on speakers’ responses with separate aspects of language as causal mediators. We illustrate the promises and challenges of this framework via a theoretical case study of the effect of an advocate’s gender on interruptions from justices during U.S. Supreme Court oral arguments. We also discuss challenges conceptualizing and operationalizing causal variables such as gender and language that comprise of many components, and we articulate technical open challenges such as temporal dependence between language mediators in conversational settings.

EACL

Changing the Mind of Transformers for Topically-Controllable Language Generation

Haw-Shiuan Chang, Jiaming Yuan, Mohit Iyyer, and Andrew McCallum

In Conference of the European Chapter of the Association for Computational Linguistics (EACL) (Oral), 2021

Paper Bib Code Poster Slides Talk

@inproceedings{chang2021changing,
  title = {Changing the Mind of Transformers for Topically-Controllable Language Generation},
  abbr = {EACL},
  bibtex_show = {true},
  author = {Chang, Haw-Shiuan and Yuan, Jiaming and Iyyer, Mohit and McCallum, Andrew},
  booktitle = {Conference of the European Chapter of the Association for Computational Linguistics (EACL) (Oral)},
  year = {2021},
  paper = {https://arxiv.org/abs/2103.15335},
  code = {https://github.com/iesl/interactive_LM},
  video = {https://slideslive.com/38954487/changing-the-mind-of-transformers-for-topicallycontrollable-language-generation},
  poster = {https://f6d60bef-de96-4b94-b613-4913f88f2f0f.filesusr.com/ugd/e150d8_87e429adfcb9478e86a55033df144458.pdf},
  slides = {https://f6d60bef-de96-4b94-b613-4913f88f2f0f.filesusr.com/ugd/e150d8_8212c213a26a4c36acc69989aec2399c.key?dn=EACL_interactive_LM.key}
}

EACL

Multi-facet Universal Schema

Rohan Paul*, Haw-Shiuan Chang*, and Andrew McCallum

In Conference of the European Chapter of the Association for Computational Linguistics (EACL) (Oral), 2021

Paper Bib Code Poster Slides Talk

@inproceedings{chang2021multi-facet,
  title = {Multi-facet Universal Schema},
  abbr = {EACL},
  bibtex_show = {true},
  author = {Paul*, Rohan and Chang*, Haw-Shiuan and McCallum, Andrew},
  booktitle = {Conference of the European Chapter of the Association for Computational Linguistics (EACL) (Oral)},
  year = {2021},
  paper = {https://arxiv.org/abs/2103.15339},
  code = {https://github.com/rohanpaul11/multifacet-re},
  video = {https://slideslive.com/38954382/multifacet-universal-schema},
  poster = {https://f6d60bef-de96-4b94-b613-4913f88f2f0f.filesusr.com/ugd/e150d8_97d81dbfca604d07b825f2214805166a.pdf},
  slides = {https://f6d60bef-de96-4b94-b613-4913f88f2f0f.filesusr.com/ugd/e150d8_b646fb0d748f43f788131a2dee5e2572.key?dn=EACL_multi-facet_RE_small.key}
}

EMNLP

Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Trapit Bansal, Karthick Prasad Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, and Andrew McCallum

In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks. However, the efficacy of meta-learning crucially depends on the distribution of tasks available for training, and this is often assumed to be known a priori or constructed from limited supervised datasets. In this work, we aim to provide task distributions for meta-learning by considering self-supervised tasks automatically proposed from unlabeled text, to enable large-scale meta-learning in NLP. We design multiple distributions of self-supervised tasks by considering important aspects of task diversity, difficulty, type, domain, and curriculum, and investigate how they affect meta-learning performance. Our analysis shows that all these factors meaningfully alter the task distribution, some inducing significant improvements in downstream few-shot accuracy of the meta-learned models. Empirically, results on 20 downstream tasks show significant improvements in few-shot learning – adding up to +4.2% absolute accuracy (on average) to the previous unsupervised meta-learning method, and perform comparably to supervised methods on the FewRel 2.0 benchmark.
EMNLP

Making Better Use of Unlabeled Data with Task Augmentation and Self-training

Tu Vu, Thang Luong, Quoc Le, Grady Simon, and Mohit Iyyer

In forthcoming EMNLP, 2021
EMNLP

Case-based Reasoning for Natural Language Questions over Knowledge Bases

Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay-Yoon Lee, Liz Tan, Lazaros Polymenakos, and Andrew McCallum

In forthcoming EMNLP, 2021
EMNLP

Open Aspect Target Sentiment Classification with Natural Language Prompts

Ronald Seoh, Ian Birle, Mrinal Tak, Haw-Shiuan Chang, Brian Pinette, and Alfred Hough

In forthcoming EMNLP, 2021
EMNLP

Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

Trapit Bansal, Karthick Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, and Andrew McCallum

In forthcoming EMNLP, 2021
EMNLP

MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text

Tim O’Gorman, Zach Jensen, Sheshera Mysore, Kevin Huang, Rubayyat Mahbub, Elsa Olivetti, and Andrew McCallum

In forthcoming EMNLP, 2021
EMNLP

Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

Zichao Wang, Richard Baraniuk, and Andrew Lan

In forthcoming EMNLP, 2021
EMNLP

Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

Shufan Wang, Laure Thompson, and Mohit Iyyer

In forthcoming EMNLP, 2021
EMNLP

Improved Latent Tree Induction with Distant Supervision via Span Constraints

Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, and Andrew McCallum

In forthcoming EMNLP, 2021
EMNLP

IGA: An Intent-Guided Authoring Assistant

Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, and Mohit Iyyer

In forthcoming EMNLP, 2021
EMNLP

Do Long-Range Language Models Actually Use Long-Range Context?

Simeng Sun, Kalpesh Krishna, Andrew Mattarella-Micke, and Mohit Iyyer

In forthcoming EMNLP, 2021
EMNLP

The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation

Marzena Karpinska, Nader Akoury, and Mohit Iyyer

In forthcoming EMNLP, 2021
EMNLP demo

Box Embeddings: An open-source library for representation learning using geometric structures

Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, and Andrew McCallum

In forthcoming EMNLP demo, 2021

Find. of ACL

Predicting In-Hospital Mortality by Combining Clinical Notes with Time-Series Data.

Iman Deznabi, Mohit Iyyer, and Madalina Fiterau

In Findings of the Association for Computational Linguistics, 2021

Paper Bib

@inproceedings{clinical21,
  abbr = {Find. of ACL},
  bibtex_show = {true},
  author = {Deznabi, Iman and Iyyer, Mohit and Fiterau, Madalina},
  booktitle = {Findings of the Association for Computational Linguistics},
  year = {2021},
  title = {Predicting In-Hospital Mortality by Combining Clinical Notes with Time-Series Data.},
  paper = {https://aclanthology.org/2021.findings-acl.352.pdf}
}

Find. of ACL

Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence

Andrew Halterman, Katherine Keith, Sheikh Sarwar, and Brendan O’Connor

In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021

Paper Bib

@inproceedings{halterman-etal-2021-corput,
  bibtex_show = {true},
  abbr = {Find. of ACL},
  paper = {https://aclanthology.org/2021.findings-acl.371.pdf},
  address = {Online},
  author = {Halterman, Andrew and Keith, Katherine and Sarwar, Sheikh and O{'}Connor, Brendan},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  date-added = {2021-08-11 09:39:43 -0400},
  date-modified = {2021-08-11 09:39:43 -0400},
  doi = {10.18653/v1/2021.findings-acl.371},
  month = aug,
  pages = {4240--4253},
  publisher = {Association for Computational Linguistics},
  title = {Corpus-Level Evaluation for Event {QA}: The {I}ndia{P}olice{E}vents Corpus Covering the 2002 {G}ujarat Violence},
  url = {https://aclanthology.org/2021.findings-acl.371},
  year = {2021},
  bdsk-url-1 = {https://aclanthology.org/2021.findings-acl.371},
  bdsk-url-2 = {https://doi.org/10.18653/v1/2021.findings-acl.371}
}

NAACL
Hurdles to Progress in Long-form Question Answering

Kalpesh Krishna, Aurko Roy, and Mohit Iyyer

In North American Association for Computational Linguistics, 2021

Paper Bib Code
@inproceedings{lfqa21, abbr = {NAACL}, bibtex_show = {true}, author = {Krishna, Kalpesh and Roy, Aurko and Iyyer, Mohit}, booktitle = {North American Association for Computational Linguistics}, year = {2021}, title = {Hurdles to Progress in Long-form Question Answering}, paper = {https://arxiv.org/abs/2103.06332}, code = {https://github.com/martiansideofthemoon/hurdles-longform-qa} }
NAACL
TABBIE: Pretrained Representations of Tabular Data

Hiroshi Iida, June Thai, Varun Manjunatha, and Mohit Iyyer

In North American Association for Computational Linguistics, 2021

Paper Bib Code
@inproceedings{tabbie21, abbr = {NAACL}, bibtex_show = {true}, author = {Iida, Hiroshi and Thai, June and Manjunatha, Varun and Iyyer, Mohit}, booktitle = {North American Association for Computational Linguistics}, year = {2021}, title = {TABBIE: Pretrained Representations of Tabular Data}, paper = {https://arxiv.org/abs/2105.02584}, code = {https://github.com/SFIG611/tabbie} }

NAACL

Revisiting Simple Neural Probabilistic Language Models

Simeng Sun and Mohit Iyyer

In North American Association for Computational Linguistics, 2021

Paper Bib Code

@inproceedings{stupidlm21,
  abbr = {NAACL},
  bibtex_show = {true},
  author = {Sun, Simeng and Iyyer, Mohit},
  booktitle = {North American Association for Computational Linguistics},
  year = {2021},
  title = {Revisiting Simple Neural Probabilistic Language Models},
  paper = {https://arxiv.org/abs/2104.03474},
  code = {https://github.com/SimengSun/revisit-nplm}
}

NeurIPS

Capacity and Bias of Learned Geometric Embeddings for Directed Graphs

Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L Clarkson, and Andrew McCallum

Advances in Neural Information Processing Systems, 2021
UAI

Exact and approximate hierarchical clustering using A

Craig S Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, and Andrew McCallum

In Uncertainty in Artificial Intelligence, 2021
UAI

Min/max stability and box distributions

Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, and Andrew McCallum

In Uncertainty in Artificial Intelligence, 2021

UnImplicit

Challenges in Detecting Null Relativizers in African American Language for Sociolinguistic and Psycholinguistic Applications

Anissa Neal, Brendan O’Connor, and Lisa Green

Unpublished abstract presented at UnImplicit: The First Workshop on Understanding Implicit and Underspecified Language at ACL-IJCNLP, 2021

Paper Bib

@conference{Neal2021RC,
  bibtex_show = {true},
  abbr = {UnImplicit},
  paper = {https://people.umass.edu/anneal/files/ACL_IJCNLP_Challenges_in_detecting_null_relativizers_2021.pdf},
  author = {Neal, Anissa and {O'Connor}, Brendan and Green, Lisa},
  info = {Unpublished abstract presented at <i>UnImplicit: The First Workshop on Understanding Implicit and Underspecified Language at ACL-IJCNLP</i>},
  date-added = {2021-08-11 09:41:48 -0400},
  date-modified = {2021-08-11 09:46:18 -0400},
  title = {Challenges in Detecting Null Relativizers in African American Language for Sociolinguistic and Psycholinguistic Applications},
  year = {2021}
}

2020

AAAI
Simultaneously linking entities and extracting relations from biomedical text without mention-level supervision

Trapit Bansal, Pat Verga, Neha Choudhary, and Andrew McCallum

In Proceedings of the AAAI Conference on Artificial Intelligence, 2020

Bib
@inproceedings{bansal2020simultaneously, title = {Simultaneously linking entities and extracting relations from biomedical text without mention-level supervision}, abbr = {AAAI}, bibtex_show = {true}, author = {Bansal, Trapit and Verga, Pat and Choudhary, Neha and McCallum, Andrew}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {34}, number = {05}, pages = {7407--7414}, year = {2020} }
ACL
Hard-Coded Gaussian Attention for Neural Machine Translation

Weiqiu You, Simeng Sun, and Mohit Iyyer

In Association for Computational Linguistics, 2020

Paper Bib Code
@inproceedings{acl2020, abbr = {ACL}, bibtex_show = {true}, author = {You, Weiqiu and Sun, Simeng and Iyyer, Mohit}, booktitle = {Association for Computational Linguistics}, year = {2020}, title = {Hard-Coded Gaussian Attention for Neural Machine Translation}, paper = {https://arxiv.org/abs/2005.00742}, code = {https://github.com/fallcat/stupidNMT} }
ACL
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates

Katherine Keith, David Jensen, and Brendan O’Connor

In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

Paper Bib

Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that influence both potential causes and potential effects. Unmeasured or latent confounders can bias causal estimates, and this has motivated interest in measuring potential confounders from observed text. For example, an individual’s entire history of social media posts or the content of a news article could provide a rich measurement of multiple confounders.Yet, methods and applications for this problem are scattered across different communities and evaluation practices are inconsistent.This review is the first to gather and categorize these examples and provide a guide to data-processing and evaluation decisions. Despite increased attention on adjusting for confounding using text, there are still many open problems, which we highlight in this paper.
@inproceedings{keith-etal-2020-text, bibtex_show = {true}, abbr = {ACL}, paper = {https://aclanthology.org/2020.acl-main.474.pdf}, address = {Online}, author = {Keith, Katherine and Jensen, David and O{'}Connor, Brendan}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, date-added = {2021-08-11 09:45:48 -0400}, date-modified = {2021-08-11 09:45:48 -0400}, doi = {10.18653/v1/2020.acl-main.474}, month = jul, pages = {5332--5344}, publisher = {Association for Computational Linguistics}, title = {Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates}, url = {https://aclanthology.org/2020.acl-main.474}, year = {2020}, bdsk-url-1 = {https://aclanthology.org/2020.acl-main.474}, bdsk-url-2 = {https://doi.org/10.18653/v1/2020.acl-main.474} }
CLEF
Unsupervised Pre-training for Biomedical Question Answering

Vaishnavi Kommaraju, Karthick Gunasekaran, Kun Li, Trapit Bansal, Andrew McCallum, Ivana Williams, and Ana-Maria Istrate

In CLEF (Working Notes), 2020

Paper Bib
@inproceedings{DBLP:conf/clef/KommarajuGLBMWI20, abbr = {CLEF}, bibtex_show = {true}, author = {Kommaraju, Vaishnavi and Gunasekaran, Karthick and Li, Kun and Bansal, Trapit and McCallum, Andrew and Williams, Ivana and Istrate, Ana-Maria}, title = {Unsupervised Pre-training for Biomedical Question Answering}, year = {2020}, cdate = {1577836800000}, paper = {http://ceur-ws.org/Vol-2696/paper_144.pdf}, booktitle = {CLEF (Working Notes)}, crossref = {conf/clef/2020w} }
COLING
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks

Trapit Bansal, Rishikesh Jha, and Andrew McCallum

In Proceedings of the 28th International Conference on Computational Linguistics, 2020

Bib
@inproceedings{bansal2020learning, title = {Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks}, abbr = {COLING}, bibtex_show = {true}, author = {Bansal, Trapit and Jha, Rishikesh and McCallum, Andrew}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, pages = {5108--5123}, year = {2020} }
ECIR
Weakly-Supervised Open-Retrieval Conversational Question Answering

Chen Qu, Liu Yang, Cen Chen, W. Bruce Croft, Kalpesh Krishna, and Mohit Iyyer

In European Conference on Information Retrieval, 2020

Paper Bib
@inproceedings{ecir20, abbr = {ECIR}, bibtex_show = {true}, author = {Qu, Chen and Yang, Liu and Chen, Cen and Croft, W. Bruce and Krishna, Kalpesh and Iyyer, Mohit}, booktitle = {European Conference on Information Retrieval}, year = {2020}, title = {Weakly-Supervised Open-Retrieval Conversational Question Answering}, paper = {https://arxiv.org/abs/2103.02537} }
EMNLP
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks

Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, and Andrew McCallum

In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020

Bib
@inproceedings{bansal2020self, title = {Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks}, abbr = {EMNLP}, bibtex_show = {true}, author = {Bansal, Trapit and Jha, Rishikesh and Munkhdalai, Tsendsuren and McCallum, Andrew}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, pages = {522--534}, year = {2020} }
EMNLP
Exploring and Predicting Transferability across NLP Tasks

Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2020

Paper Bib Code
@inproceedings{transfer20, abbr = {EMNLP}, bibtex_show = {true}, author = {Vu, Tu and Wang, Tong and Munkhdalai, Tsendsuren and Sordoni, Alessandro and Trischler, Adam and Mattarella-Micke, Andrew and Maji, Subhransu and Iyyer, Mohit}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2020}, title = {Exploring and Predicting Transferability across NLP Tasks}, paper = {https://arxiv.org/abs/2005.00770}, code = {https://github.com/tuvuumass/task-transferability} }
EMNLP
Reformulating Unsupervised Style Transfer as Paraphrase Generation

Kalpesh Krishna, John Wieting, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2020

Paper Bib Code
@inproceedings{style20, abbr = {EMNLP}, bibtex_show = {true}, author = {Krishna, Kalpesh and Wieting, John and Iyyer, Mohit}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2020}, title = {Reformulating Unsupervised Style Transfer as Paraphrase Generation}, paper = {https://arxiv.org/abs/2010.05700}, code = {http://style.cs.umass.edu/} }
EMNLP
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2020

Paper Bib Code
@inproceedings{storium20, abbr = {EMNLP}, bibtex_show = {true}, author = {Akoury, Nader and Wang, Shufan and Whiting, Josh and Hood, Stephen and Peng, Nanyun and Iyyer, Mohit}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2020}, title = {STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation}, paper = {https://arxiv.org/abs/2010.01717}, code = {https://storium.cs.umass.edu/} }
EMNLP
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders

Andrew Drozdov, Subendhu Rongali, Yi-Pei Chen, Tim O’Gorman, Mohit Iyyer, and Andrew McCallum

In Empirical Methods in Natural Language Processing, 2020

Paper Bib
@inproceedings{sdiora20, abbr = {EMNLP}, bibtex_show = {true}, author = {Drozdov, Andrew and Rongali, Subendhu and Chen, Yi-Pei and O'Gorman, Tim and Iyyer, Mohit and McCallum, Andrew}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2020}, title = {Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders}, paper = {https://mrdrozdov.github.io/static/papers/sdiora.pdf} }
ICLR
Thieves on Sesame Street! Model Extraction of BERT-based APIs.

Kalpesh Krishna, Gaurav Singh Tomar, Ankur Parikh, Nicolas Papernot, and Mohit Iyyer

In International Conference on Learning Representations, 2020

Paper Bib Code
@inproceedings{thieves20, abbr = {ICLR}, bibtex_show = {true}, author = {Krishna, Kalpesh and Tomar, Gaurav Singh and Parikh, Ankur and Papernot, Nicolas and Iyyer, Mohit}, booktitle = {International Conference on Learning Representations}, year = {2020}, title = {Thieves on Sesame Street! Model Extraction of BERT-based APIs.}, paper = {https://arxiv.org/abs/1910.12366}, code = {https://github.com/google-research/language/tree/master/language/bert_extraction} }
LREC
Which Evaluations Uncover Sense Representations that Actually Make Sense?

Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater

In Language Resources and Evaluation Conference, 2020

Paper Bib
@inproceedings{lrec2020, abbr = {LREC}, bibtex_show = {true}, author = {Guo, Fenfei and Boyd-Graber, Jordan and Iyyer, Mohit and Findlater, Leah}, booktitle = {Language Resources and Evaluation Conference}, year = {2020}, title = {Which Evaluations Uncover Sense Representations that Actually Make Sense?}, paper = {https://arxiv.org/abs/1804.08077} }
ML
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition

Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, and Andrew McCallum

Machine Learning, 2020

Paper Bib Slides Talk
@article{chang2019ovecoming, abbr = {ML}, bibtex_show = {true}, author = {Chang, Haw-Shiuan and Vembu, Shankar and Mohan, Sunil and Uppaal, Rheeya and McCallum, Andrew}, journal = {Machine Learning}, paper = {http://arxiv.org/abs/1911.07335}, title = {Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition}, year = {2020}, doi = {10.1007/s10994-020-05897-1}, publisher = {Springer}, video = {https://slideslive.com/38933012/using-error-decay-prediction-to-overcome-practical-issues-of-deep-active-learning-for-named-entity-recognition}, slides = {https://docs.google.com/presentation/d/1h3bI1dS8-vS5ItcfGOHN3J7Wi7dnWWBf0gA4-yBXrk4/edit?usp=sharing} }
NLP+CSS
Analyzing Gender Bias within Narrative Tropes

Dhruvil Gala, Mohammad Omar Khursheed, Hannah Lerner, Brendan O’Connor, and Mohit Iyyer

In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020

Paper Bib Code
@inproceedings{tropes20, abbr = {NLP+CSS}, bibtex_show = {true}, author = {Gala, Dhruvil and Khursheed, Mohammad Omar and Lerner, Hannah and O'Connor, Brendan and Iyyer, Mohit}, booktitle = {Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}, year = {2020}, title = {Analyzing Gender Bias within Narrative Tropes}, paper = {https://aclanthology.org/2020.nlpcss-1.23.pdf}, code = {https://github.com/dhruvilgala/tvtropes} }
NLP+CSS
Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty

Katherine Keith, Christoph Teichmann, Brendan O’Connor, and Edgar Meij

In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020

Paper Bib

Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an established economic index that measures economic policy uncertainty from keyword occurrences in news. This index, which is shown to correlate with firm investment, employment, and excess market returns, has had substantive impact in both the private sector and academia. Yet, as we revisit and extend the original authors’ annotations and text measurements we find interesting text-as-data methodological research questions: (1) Are annotator disagreements a reflection of ambiguity in language? (2) Do alternative text measurements correlate with one another and with measures of external predictive validity? We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index.
@inproceedings{keith-etal-2020-uncertainty, bibtex_show = {true}, abbr = {NLP+CSS}, paper = {https://aclanthology.org/2020.nlpcss-1.13.pdf}, address = {Online}, author = {Keith, Katherine and Teichmann, Christoph and O{'}Connor, Brendan and Meij, Edgar}, booktitle = {Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}, date-added = {2021-08-11 09:45:36 -0400}, date-modified = {2021-08-11 09:45:36 -0400}, doi = {10.18653/v1/2020.nlpcss-1.13}, month = nov, pages = {116--131}, publisher = {Association for Computational Linguistics}, title = {Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty}, url = {https://aclanthology.org/2020.nlpcss-1.13}, year = {2020}, bdsk-url-1 = {https://aclanthology.org/2020.nlpcss-1.13}, bdsk-url-2 = {https://doi.org/10.18653/v1/2020.nlpcss-1.13} }
SIGIR
Open-Retrieval Conversational Question Answering

Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, and Mohit Iyyer

In 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020

Paper Bib
@inproceedings{openconvqa, abbr = {SIGIR}, bibtex_show = {true}, author = {Qu, Chen and Yang, Liu and Chen, Cen and Qiu, Minghui and Croft, W. Bruce and Iyyer, Mohit}, booktitle = {43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, year = {2020}, title = {Open-Retrieval Conversational Question Answering}, paper = {https://arxiv.org/abs/2005.11364} }

arXiv

Topic Modeling with Contextualized Word Representation Clusters

Laure Thompson and David Mimno

arXiv preprint arXiv:2010.12626, 2020

Paper Bib

@article{thompson2020topic,
  abbr = {arXiv},
  bibtex_show = {true},
  paper = {https://arxiv.org/pdf/2010.12626.pdf},
  title = {Topic Modeling with Contextualized Word Representation Clusters},
  author = {Thompson, Laure and Mimno, David},
  year = {2020},
  journal = {arXiv preprint arXiv:2010.12626}
}

2019

ACL
Optimal Transport-based Alignment of Learned Character Representations for String Similarity

Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, and Andrew McCallum

In Association of Computational Linguistics (ACL), 2019

Bib
@inproceedings{tam2019optimal, title = {Optimal Transport-based Alignment of Learned Character Representations for String Similarity}, abbr = {ACL}, bibtex_show = {true}, author = {Tam, Derek and Monath, Nicholas and Kobren, Ari and Traylor, Aaron and Das, Rajarshi and McCallum, Andrew}, booktitle = {Association of Computational Linguistics (ACL)}, year = {2019} }
ACL
A2N: Attending to Neighbors for Knowledge Graph Inference

Trapit Bansal, Da-Cheng Juan, Sujith Ravi, and Andrew McCallum

In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

Paper Bib
@inproceedings{bansal-etal-2019-a2n, title = {{A}2{N}: Attending to Neighbors for Knowledge Graph Inference}, abbr = {ACL}, bibtex_show = {true}, author = {Bansal, Trapit and Juan, Da-Cheng and Ravi, Sujith and McCallum, Andrew}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, month = jul, year = {2019}, address = {Florence, Italy}, publisher = {Association for Computational Linguistics}, paper = {https://www.aclweb.org/anthology/P19-1431}, doi = {10.18653/v1/P19-1431}, pages = {4387--4392} }
ACL
Syntactically Supervised Transformers for Faster Neural Machine Translation

Nader Akoury, Kalpesh Krishna, and Mohit Iyyer

In Association for Computational Linguistics, 2019

Paper Bib Code
@inproceedings{synst2019, abbr = {ACL}, bibtex_show = {true}, author = {Akoury, Nader and Krishna, Kalpesh and Iyyer, Mohit}, booktitle = {Association for Computational Linguistics}, year = {2019}, title = {Syntactically Supervised Transformers for Faster Neural Machine Translation}, paper = {https://arxiv.org/abs/1906.02780}, code = {https://github.com/dojoteef/synst} }

ACL

Generating Question-Answer Hierarchies

Kalpesh Krishna and Mohit Iyyer

In Association for Computational Linguistics, 2019

Paper Bib Code

@inproceedings{squash2019,
  abbr = {ACL},
  bibtex_show = {true},
  author = {Krishna, Kalpesh and Iyyer, Mohit},
  booktitle = {Association for Computational Linguistics},
  year = {2019},
  title = {Generating Question-Answer Hierarchies},
  paper = {https://arxiv.org/abs/1906.02622},
  code = {http://squash.cs.umass.edu/}
}

ACL

Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification

Tu Vu and Mohit Iyyer

In Association for Computational Linguistics, 2019

Paper Bib Code

@inproceedings{paraemb2019,
  abbr = {ACL},
  bibtex_show = {true},
  author = {Vu, Tu and Iyyer, Mohit},
  booktitle = {Association for Computational Linguistics},
  year = {2019},
  title = {Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification},
  paper = {https://arxiv.org/abs/1906.03656},
  code = {https://github.com/tuvuumass/SCoPE}
}

AKBC
Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction

Ari Kobren, Nicholas Monath, and Andrew McCallum

In Automated Knowledge Base Construction (AKBC), 2019

Paper Bib
@inproceedings{kobren2019feedback, title = {Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction}, abbr = {AKBC}, bibtex_show = {true}, author = {Kobren, Ari and Monath, Nicholas and McCallum, Andrew}, booktitle = {Automated Knowledge Base Construction (AKBC)}, year = {2019}, paper = {https://openreview.net/forum?id=SygLHbcapm} }
CIKM
Attentive History Selection for Conversational Question Answering

Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, and Mohit Iyyer

In Conference on Information and Knowledge Management, 2019

Paper Bib
@inproceedings{cikm2019, abbr = {CIKM}, bibtex_show = {true}, author = {Qu, Chen and Yang, Liu and Qiu, Minghui and Zhang, Yongfeng and Chen, Cen and Croft, W. Bruce and Iyyer, Mohit}, booktitle = {Conference on Information and Knowledge Management}, year = {2019}, title = {Attentive History Selection for Conversational Question Answering}, paper = {https://arxiv.org/abs/1908.09456} }
EMNLP
Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts.

Jack Merullo, Luke Yeh, Abram Handler, Alvin Grissom II, Brendan O’Connor, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2019

Paper Bib Code
@inproceedings{football2019, abbr = {EMNLP}, bibtex_show = {true}, author = {Merullo, Jack and Yeh, Luke and Handler, Abram and II, Alvin Grissom and O'Connor, Brendan and Iyyer, Mohit}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2019}, title = {Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts.}, paper = {https://arxiv.org/abs/1909.03343}, code = {https://github.com/jmerullo/football} }
EMNLP
Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders

Andrew Drozdov, Patrick Verga, Yi-Pei Chen, Mohit Iyyer, and Andrew McCallum

In Empirical Methods in Natural Language Processing, 2019

Paper Bib
@inproceedings{diora2_2019, abbr = {EMNLP}, bibtex_show = {true}, author = {Drozdov, Andrew and Verga, Patrick and Chen, Yi-Pei and Iyyer, Mohit and McCallum, Andrew}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2019}, title = {Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders}, paper = {https://www.aclweb.org/anthology/D19-1161.pdf} }

EMNLP

Query-focused Sentence Compression in Linear Time

Abram Handler and Brendan O’Connor

In Proceedings of EMNLP, 2019

Paper Bib

@inproceedings{Handler2019Compression,
  bibtex_show = {true},
  abbr = {EMNLP},
  paper = {https://aclanthology.org/D19-1612.pdf},
  author = {Handler, Abram and O'Connor, Brendan},
  booktitle = {Proceedings of {EMNLP}},
  date-added = {2019-09-03 01:25:49 +0000},
  date-modified = {2019-09-03 01:26:13 +0000},
  title = {Query-focused Sentence Compression in Linear Time},
  year = {2019}
}

ICLR
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum

In International Conference on Learning Representations (ICLR), 2019

Paper Bib
@inproceedings{das2019multi-step, title = {Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering}, abbr = {ICLR}, bibtex_show = {true}, author = {Das, Rajarshi and Dhuliawala, Shehzaad and Zaheer, Manzil and McCallum, Andrew}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2019}, paper = {https://openreview.net/forum?id=HkfPSh05K7} }
ICLR
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, and Andrew McCallum

In International Conference on Learning Representations (ICLR), 2019

Paper Bib
@inproceedings{das2019building, title = {Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension}, abbr = {ICLR}, bibtex_show = {true}, author = {Das, Rajarshi and Dhuliawala, Shehzaad and Zaheer, Manzil and McCallum, Andrew}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2019}, paper = {https://openreview.net/forum?id=S1lhbnRqF7} }
ICLR
Smoothing the Geometry of Box Embeddings

Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, and Andrew McCallum

In International Conference on Learning Representations (ICLR) (Oral), 2019

Paper Bib
@inproceedings{li2019smoothing, title = {Smoothing the Geometry of Box Embeddings}, abbr = {ICLR}, bibtex_show = {true}, author = {Li, Xiang and Vilnis, Luke and Zhang, Dongxu and Boratko, Michael and McCallum, Andrew}, booktitle = {International Conference on Learning Representations (ICLR) (Oral)}, year = {2019}, paper = {https://openreview.net/forum?id=H1xSNiRcF7} }
ICML
Supervised Hierarchical Clustering with Exponential Linkage

Nishant Yadav, Ari Kobren, Nicholas Monath, and Andrew McCallum

In International Conference on Machine Learning (ICML), 2019

Paper Bib Code
@inproceedings{yadav2019supervised, title = {Supervised Hierarchical Clustering with Exponential Linkage}, abbr = {ICML}, bibtex_show = {true}, author = {Yadav, Nishant and Kobren, Ari and Monath, Nicholas and McCallum, Andrew}, booktitle = {International Conference on Machine Learning (ICML)}, year = {2019}, paper = {http://proceedings.mlr.press/v97/yadav19a/yadav19a.pdf}, code = {https://github.com/iesl/expLinkage} }
KDD
Scalable Hierarchical Clustering with Tree Grafting

Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael Glass, and Andrew McCallum

In International Conference on Knowledge Discovery and Data Mining (KDD), 2019

Bib
@inproceedings{monath2019grinch, title = {Scalable Hierarchical Clustering with Tree Grafting}, abbr = {KDD}, bibtex_show = {true}, author = {Monath, Nicholas and Kobren, Ari and Krishnamurthy, Akshay and Glass, Michael and McCallum, Andrew}, booktitle = {International Conference on Knowledge Discovery and Data Mining (KDD)}, year = {2019} }
KDD
Paper Matching with Local Fairness Constraints

Ari Kobren, Barna Saha, and Andrew McCallum

In International Conference on Knowledge Discovery and Data Mining (KDD), 2019

Paper Bib Code
@inproceedings{kobren2019matching, title = {Paper Matching with Local Fairness Constraints}, abbr = {KDD}, bibtex_show = {true}, author = {Kobren, Ari and Saha, Barna and McCallum, Andrew}, booktitle = {International Conference on Knowledge Discovery and Data Mining (KDD)}, year = {2019}, code = {https://github.com/iesl/fair-matching}, paper = {https://arxiv.org/abs/1905.11924} }
KDD
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, and Amr Ahmed

In International Conference on Knowledge Discovery and Data Mining (KDD), 2019

Bib
@inproceedings{monath2019gradient, title = {Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space}, abbr = {KDD}, bibtex_show = {true}, author = {Monath, Nicholas and Zaheer, Manzil and Silva, Daniel and McCallum, Andrew and Ahmed, Amr}, booktitle = {International Conference on Knowledge Discovery and Data Mining (KDD)}, year = {2019} }
LA+ACL
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures

Sheshera Mysore, Zach Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, and Elsa Olivetti

In Proceedings of the 13th Linguistic Annotation Workshop at ACL, 2019

Paper Bib
@inproceedings{mysore2019msannlaw, title = {The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures}, abbr = {LA+ACL}, bibtex_show = {true}, author = {Mysore, Sheshera and Jensen, Zach and Kim, Edward and Huang, Kevin and Chang, Haw-Shiuan and Strubell, Emma and Flanigan, Jeffrey and McCallum, Andrew and Olivetti, Elsa}, booktitle = {Proceedings of the 13th Linguistic Annotation Workshop at ACL}, year = {2019}, paper = {https://sigann.github.io/LAW-XIII-2019/pdf/W19-4007.pdf} }
NAACL
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference

Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard, Luna Dong, and Andrew McCallum

In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2019

Paper Bib
@inproceedings{zhang2019openki, title = {OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference}, abbr = {NAACL}, bibtex_show = {true}, author = {Zhang, Dongxu and Mukherjee, Subhabrata and Lockard, Colin and Dong, Luna and McCallum, Andrew}, booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL)}, pages = {762--772}, year = {2019}, paper = {https://www.aclweb.org/anthology/N19-1083} }
NAACL
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders

Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, and Andrew McCallum

In North American Association for Computational Linguistics, 2019

Paper Bib Code
@inproceedings{DIORA2019, abbr = {NAACL}, bibtex_show = {true}, author = {Drozdov, Andrew and Verga, Patrick and Yadav, Mohit and Iyyer, Mohit and McCallum, Andrew}, booktitle = {North American Association for Computational Linguistics}, year = {2019}, title = {Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders}, paper = {https://arxiv.org/abs/1904.02142}, code = {https://github.com/iesl/diora} }

NAACL

Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism

Shufan Wang and Mohit Iyyer

In North American Association for Computational Linguistics, 2019

Paper Bib

@inproceedings{Wang2019,
  abbr = {NAACL},
  bibtex_show = {true},
  author = {Wang, Shufan and Iyyer, Mohit},
  booktitle = {North American Association for Computational Linguistics},
  year = {2019},
  title = {Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism},
  paper = {https://arxiv.org/abs/1904.08386}
}

SCiL

Preface: SCiL 2019 Editors’ Note

Gaja Jarosz, Max Nelson, Brendan O’Connor, and Joe Pater

Proceedings of the Society for Computation in Linguistics, 2019

Paper Bib

@article{Jarosz2019SCiL,
  bibtex_show = {true},
  abbr = {SCiL},
  paper = {https://doi.org/10.7275/ntf6-xx21},
  author = {Jarosz, Gaja and Nelson, Max and O'Connor, Brendan and Pater, Joe},
  date-added = {2019-09-03 01:37:37 +0000},
  date-modified = {2021-08-11 10:08:08 -0400},
  journal = {Proceedings of the Society for Computation in Linguistics},
  number = {1},
  title = {Preface: {SCiL} 2019 Editors' Note},
  volume = {2},
  year = {2019}
}

SIGIR
BERT with History Modeling for Conversational Question Answering

Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, and Mohit Iyyer

In 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

Paper Bib Code
@inproceedings{ConvQA2019, abbr = {SIGIR}, bibtex_show = {true}, author = {Qu, Chen and Yang, Liu and Qiu, Minghui and Croft, W. Bruce and Zhang, Yongfeng and Iyyer, Mohit}, booktitle = {42nd International ACM SIGIR Conference on Research and Development in Information Retrieval}, year = {2019}, title = {BERT with History Modeling for Conversational Question Answering}, paper = {https://arxiv.org/abs/1905.05412}, code = {https://github.com/prdwb/bert_hae} }

arXiv

Human acceptability judgements for extractive sentence compression

Abram Handler, Brian Dillon, and Brendan O’Connor

arXiv preprint arXiv:1902.00489, 2019

Paper Bib

@article{Handler2019Human,
  bibtex_show = {true},
  abbr = {arXiv},
  paper = {https://arxiv.org/pdf/1902.00489.pdf},
  author = {Handler, Abram and Dillon, Brian and O'Connor, Brendan},
  date-added = {2019-09-05 04:46:18 +0000},
  date-modified = {2019-09-05 04:46:24 +0000},
  journal = {arXiv preprint arXiv:1902.00489},
  title = {Human acceptability judgements for extractive sentence compression},
  year = {2019}
}

2018

ACL
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Shikhar Murty*, Patrick Verga*, Luke Vilnis, Irena Radovanovic, and Andrew McCallum (* Equal Contribution)

In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL) (Oral), 2018

Paper Bib
@inproceedings{DBLP:conf/acl/Murty18, abbr = {ACL}, bibtex_show = {true}, author = {Murty*, Shikhar and Verga*, Patrick and Vilnis, Luke and Radovanovic, Irena and indicates Equal Contribution), Andrew McCallum (*}, title = {Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking}, booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL) (Oral)}, year = {2018}, paper = {http://aclweb.org/anthology/P18-1010} }
ACL
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures

Luke Vilnis*, Xiang Li*, Shikhar Murty, and Andrew McCallum (* Equal Contribution)

In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 2018

Paper Bib
@inproceedings{DBLP:conf/acl/Vilnis18, abbr = {ACL}, bibtex_show = {true}, author = {Vilnis*, Luke and Li*, Xiang and Murty, Shikhar and indicates Equal Contribution), Andrew McCallum (*}, title = {Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures}, booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2018}, paper = {http://people.cs.umass.edu/~luke/box-lattices.pdf} }
ACL
Twitter Universal Dependency Parsing for African-American and Mainstream American English

Su Lin Blodgett, Johnny Wei, and Brendan O’Connor

In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018

Paper Bib

Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods’ impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at: http://slanglab.cs.umass.edu/TwitterAAE/.
@inproceedings{Blodgett2018Parsing, bibtex_show = {true}, abbr = {ACL}, paper = {http://www.aclweb.org/anthology/P18-1131.pdf}, address = {Melbourne, Australia}, author = {Blodgett, Su Lin and Wei, Johnny and O'Connor, Brendan}, booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, date-added = {2018-07-18 07:19:10 +0000}, date-modified = {2018-07-18 11:55:28 +0000}, month = jul, pages = {1415--1425}, publisher = {Association for Computational Linguistics}, title = {{Twitter Universal Dependency} Parsing for {African-American} and Mainstream {American English}}, url = {http://www.aclweb.org/anthology/P18-1131}, year = {2018}, bdsk-url-1 = {http://www.aclweb.org/anthology/P18-1131} }
BlackboxNLP
Evaluating Grammaticality in Seq2seq Models with a Broad Coverage HPSG Grammar: A Case Study on Machine Translation

Johnny Wei, Khiem Pham, Brendan O’Connor, and Brian Dillon

In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2018

Paper Bib

Sequence to sequence (seq2seq) models are often employed in settings where the target output is natural language. However, the syntactic properties of the language generated from these models are not well understood. We explore whether such output belongs to a formal and realistic grammar, by employing the English Resource Grammar (ERG), a broad coverage, linguistically precise HPSG-based grammar of English. From a French to English parallel corpus, we analyze the parseability and grammatical constructions occurring in output from a seq2seq translation model. Over 93% of the model translations are parseable, suggesting that it learns to generate conforming to a grammar. The model has trouble learning the distribution of rarer syntactic rules, and we pinpoint several constructions that differentiate translations between the references and our model.
@inproceedings{Wei2018HPSG, bibtex_show = {true}, abbr = {BlackboxNLP}, paper = {https://www.aclweb.org/anthology/W18-5432.pdf}, address = {Brussels, Belgium}, author = {Wei, Johnny and Pham, Khiem and O{'}Connor, Brendan and Dillon, Brian}, booktitle = {Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}}, date-added = {2019-09-05 04:54:02 +0000}, date-modified = {2019-09-05 04:54:10 +0000}, doi = {10.18653/v1/W18-5432}, month = nov, pages = {298--305}, publisher = {Association for Computational Linguistics}, title = {Evaluating Grammaticality in Seq2seq Models with a Broad Coverage {HPSG} Grammar: A Case Study on Machine Translation}, url = {https://www.aclweb.org/anthology/W18-5432}, year = {2018}, bdsk-url-1 = {https://www.aclweb.org/anthology/W18-5432}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/W18-5432} }

CIKM WS

Exploring Summary-Expanded Entity Embeddings for Entity Retrieval

Shahrzad Naseri, John Foley, James Allan, and Brendan O’Connor

2018

Paper Bib

@inproceedings{Naseri2018Entity,
  bibtex_show = {true},
  abbr = {CIKM WS},
  paper = {https://ciir-publications.cs.umass.edu/getpdf.php?id=1362},
  author = {Naseri, Shahrzad and Foley, John and Allan, James and O'Connor, Brendan},
  booktitle = {CEUR Workshop Proceedings (workshop at CIKM)},
  date-added = {2019-09-02 17:47:39 +0000},
  date-modified = {2021-08-11 10:04:56 -0400},
  title = {Exploring Summary-Expanded Entity Embeddings for Entity Retrieval},
  type = {IR},
  year = {2018}
}

COLING

Authorless Topic Models: Biasing Models Away from Known Structure

Laure Thompson and David Mimno

In Proceedings of the 27th International Conference on Computational Linguistics, 2018

Paper Bib

@inproceedings{thompson-mimno-2018-authorless,
  abbr = {COLING},
  bibtex_show = {true},
  title = {Authorless Topic Models: Biasing Models Away from Known Structure},
  author = {Thompson, Laure and Mimno, David},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month = aug,
  year = {2018},
  address = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  paper = {https://aclanthology.org/C18-1329},
  pages = {3903--3914}
}

CoNLL
Embedded-State Latent Conditional Random Fields for Sequence Labeling

Dung Thai, Sree Harsha Ramesh, Shikhar Murty, Luke Vilnis, and Andrew McCallum

In Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL), 2018

Paper Bib
@inproceedings{thai2018embedded, title = {Embedded-State Latent Conditional Random Fields for Sequence Labeling}, abbr = {CoNLL}, bibtex_show = {true}, author = {Thai, Dung and Ramesh, Sree Harsha and Murty, Shikhar and Vilnis, Luke and McCallum, Andrew}, booktitle = {Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL)}, year = {2018}, paper = {https://arxiv.org/abs/1809.10835} }

ECIR

A Neural Passage Model for Ad-hoc Document Retrieval

Qingyao Ai, Brendan O’Connor, and W. Bruce Croft

In Advances in Information Retrieval. ECIR 2018 (European Conference on Information Retrieval), 2018

Bib PDF

@inproceedings{Ai2018Passage,
  bibtex_show = {true},
  abbr = {ECIR},
  pdf = {https://arxiv.org/pdf/2103.09306.pdf},
  doi = {https://doi.org/10.1007/978-3-319-76941-7_41},
  author = {Ai, Qingyao and O'Connor, Brendan and Croft, W.\ Bruce},
  booktitle = {Advances in Information Retrieval. ECIR 2018 (European Conference on Information Retrieval)},
  date-added = {2019-09-03 01:28:53 +0000},
  date-modified = {2019-09-03 01:29:21 +0000},
  title = {A Neural Passage Model for Ad-hoc Document Retrieval},
  year = {2018}
}

EMNLP
Linguistically-Informed Self-Attention for Semantic Role Labeling

Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum

In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Best paper award), 2018

Paper Bib
@inproceedings{strubell2018linguistically, title = {Linguistically-Informed Self-Attention for Semantic Role Labeling}, abbr = {EMNLP}, bibtex_show = {true}, author = {Strubell, Emma and Verga, Patrick and Andor, Daniel and Weiss, David and McCallum, Andrew}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Best paper award)}, year = {2018}, paper = {https://arxiv.org/abs/1804.08199} }
EMNLP
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets

Nathan Greenberg, Trapit Bansal, Patrick Verga, and Andrew McCallum

In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Oral), 2018

Paper Bib
@inproceedings{greenberg2018marginal, title = {Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets}, abbr = {EMNLP}, bibtex_show = {true}, author = {Greenberg, Nathan and Bansal, Trapit and Verga, Patrick and McCallum, Andrew}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Oral)}, year = {2018}, paper = {http://aclweb.org/anthology/D18-1306} }
EMNLP
QuAC: Question Answering in Context

Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer

In Empirical Methods in Natural Language Processing, 2018

Paper Bib Code
@inproceedings{ChoiQuAC2018, abbr = {EMNLP}, bibtex_show = {true}, author = {Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2018}, title = {QuAC: Question Answering in Context}, paper = {https://arxiv.org/abs/1808.07036}, code = {http://quac.ai/} }
EMNLP
Pathologies of Neural Models Make Interpretation Difficult

Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber

In Empirical Methods in Natural Language Processing, 2018

Paper Bib
@inproceedings{FengRAWR2018, abbr = {EMNLP}, bibtex_show = {true}, author = {Feng, Shi and Wallace, Eric and II, Alvin Grissom and Iyyer, Mohit and Rodriguez, Pedro and Boyd-Graber, Jordan}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2018}, title = {Pathologies of Neural Models Make Interpretation Difficult}, paper = {https://arxiv.org/abs/1804.07781} }
EMNLP
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification

Kalpesh Krishna, Preethi Jyothi, and Mohit Iyyer

In Empirical Methods in Natural Language Processing, 2018

Paper Bib Code
@inproceedings{KrishnaRevisit2018, abbr = {EMNLP}, bibtex_show = {true}, author = {Krishna, Kalpesh and Jyothi, Preethi and Iyyer, Mohit}, booktitle = {Empirical Methods in Natural Language Processing}, year = {2018}, title = {Revisiting the Importance of Encoding Logic Rules in Sentiment Classification}, paper = {https://arxiv.org/abs/1808.07733}, code = {https://github.com/martiansideofthemoon/logic-rules-sentiment} }
EMNLP
Uncertainty-aware generative models for inferring document class prevalence

Katherine Keith and Brendan O’Connor

In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018

Paper Bib

Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled examples in a group—for example, the proportion of a document collection with positive sentiment. Previous work has focused on aggregating and adjusting discriminative individual classifiers to obtain prevalence point estimates. But imperfect classifier accuracy ought to be reflected in uncertainty over the predicted prevalence for scientifically valid inference. In this work, we present (1) a generative probabilistic modeling approach to prevalence estimation, and (2) the construction and evaluation of prevalence confidence intervals; in particular, we demonstrate that an off-the-shelf discriminative classifier can be given a generative re-interpretation, by backing out an implicit individual-level likelihood function, which can be used to conduct fast and simple group-level Bayesian inference. Empirically, we demonstrate our approach provides better confidence interval coverage than an alternative, and is dramatically more robust to shifts in the class prior between training and testing.
@inproceedings{Keith2018DocPropor, bibtex_show = {true}, abbr = {EMNLP}, paper = {https://www.aclweb.org/anthology/D18-1487.pdf}, address = {Brussels, Belgium}, author = {Keith, Katherine and O{'}Connor, Brendan}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, date-added = {2019-09-03 01:33:58 +0000}, date-modified = {2019-09-03 01:34:18 +0000}, doi = {10.18653/v1/D18-1487}, month = oct, pages = {4575--4585}, publisher = {Association for Computational Linguistics}, title = {Uncertainty-aware generative models for inferring document class prevalence}, url = {https://www.aclweb.org/anthology/D18-1487}, year = {2018}, bdsk-url-1 = {https://www.aclweb.org/anthology/D18-1487}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/D18-1487} }
ICLR
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum

In International Conference on Learning Representations (ICLR), 2018

Paper Bib Code
@inproceedings{das2018go, title = {Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning}, abbr = {ICLR}, bibtex_show = {true}, author = {Das, Rajarshi and Dhuliawala, Shehzaad and Zaheer, Manzil and Vilnis, Luke and Durugkar, Ishan and Krishnamurthy, Akshay and Smola, Alex and McCallum, Andrew}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2018}, paper = {https://arxiv.org/abs/1711.05851}, code = {https://github.com/shehzaadzd/MINERVA} }
NAACL
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Haw-Shiuan Chang, ZiYun Wang, Luke Vilnis, and Andrew McCallum

In Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), 2018

Paper Bib Code Poster
@inproceedings{chang2017unsupervised, title = {Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection}, abbr = {NAACL}, bibtex_show = {true}, author = {Chang, Haw-Shiuan and Wang, ZiYun and Vilnis, Luke and McCallum, Andrew}, booktitle = {Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL)}, paper = {http://arxiv.org/abs/1710.00880}, year = {2018}, code = {https://github.com/iesl/Distributional-Inclusion-Vector-Embedding}, poster = {http://docs.wixstatic.com/ugd/e150d8_925731e34b974de881cbe54f66807d36.pdf}, demo = {https://bl.ocks.org/chsu5358/raw/f08d4755b0f04e113c139a72a977df5c/} }
NAACL
Simultaneously Self-attending to All Mentions for Full-Abstract Biological Relation Extraction

Patrick Verga, Emma Strubell, and Andrew McCallum

In Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), 2018

Paper Bib Code
@inproceedings{DBLP:conf/naacl/Verga18, abbr = {NAACL}, bibtex_show = {true}, author = {Verga, Patrick and Strubell, Emma and McCallum, Andrew}, title = {Simultaneously Self-attending to All Mentions for Full-Abstract Biological Relation Extraction }, booktitle = {Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL)}, year = {2018}, paper = {https://arxiv.org/abs/1802.10569}, code = {https://github.com/patverga/bran} }
NAACL
Training Structured Prediction Energy Networks with Indirect Supervision

Amirmohammad Rooshenas, Aishwarya Kamath, and Andrew McCallum

In Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL) (Oral), 2018

Bib
@inproceedings{DBLP:conf/naacl/Rooshenas18, abbr = {NAACL}, bibtex_show = {true}, author = {Rooshenas, Amirmohammad and Kamath, Aishwarya and McCallum, Andrew}, title = {Training Structured Prediction Energy Networks with Indirect Supervision}, booktitle = {Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL) (Oral)}, year = {2018} }
NAACL
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer

In North American Association for Computational Linguistics, 2018

Paper Bib Code
@inproceedings{IyyerSCPN2018, abbr = {NAACL}, bibtex_show = {true}, author = {Iyyer, Mohit and Wieting, John and Gimpel, Kevin and Zettlemoyer, Luke}, booktitle = {North American Association for Computational Linguistics}, year = {2018}, title = {Adversarial Example Generation with Syntactically Controlled Paraphrase Networks}, paper = {https://arxiv.org/abs/1804.06059}, code = {https://github.com/miyyer/scpn} }
NAACL
Deep contextualized word representations

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer

In North American Association for Computational Linguistics, 2018

Paper Bib Code
@inproceedings{PetersELMo2018, abbr = {NAACL}, bibtex_show = {true}, author = {Peters, Matthew E. and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke}, booktitle = {North American Association for Computational Linguistics}, year = {2018}, title = {Deep contextualized word representations}, paper = {https://arxiv.org/abs/1802.05365}, code = {https://github.com/allenai/allennlp/blob/master/tutorials/how_to/elmo.md} }
NAACL
Learning to Color from Language

Varun Manjunatha, Mohit Iyyer, Jordan Boyd-Graber, and Larry Davis

In North American Association for Computational Linguistics, 2018

Paper Bib Code
@inproceedings{Manjunatha2018, abbr = {NAACL}, bibtex_show = {true}, author = {Manjunatha, Varun and Iyyer, Mohit and Boyd-Graber, Jordan and Davis, Larry}, booktitle = {North American Association for Computational Linguistics}, year = {2018}, title = {Learning to Color from Language}, paper = {https://arxiv.org/abs/1804.06026}, code = {https://github.com/superhans/colorfromlanguage} }
NAACL
Relational Summarization for Corpus Analysis

Abram Handler and Brendan O’Connor

In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018

Paper Bib

This work introduces a new problem, relational summarization, in which the goal is to generate a natural language summary of the relationship between two lexical items in a corpus, without reference to a knowledge base. Motivated by the needs of novel user interfaces, we define the task and give examples of its application. We also present a new query-focused method for finding natural language sentences which express relationships. Our method allows for summarization of more than two times more query pairs than baseline relation extractors, while returning measurably more readable output. Finally, to help guide future work, we analyze the challenges of relational summarization using both a news and a social media corpus.
@inproceedings{Handler2018Relational, bibtex_show = {true}, abbr = {NAACL}, paper = {https://www.aclweb.org/anthology/N18-1159.pdf}, address = {New Orleans, Louisiana}, author = {Handler, Abram and O{'}Connor, Brendan}, booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}, date-added = {2019-09-03 01:55:26 +0000}, date-modified = {2019-09-03 01:55:41 +0000}, doi = {10.18653/v1/N18-1159}, month = jun, pages = {1760--1769}, publisher = {Association for Computational Linguistics}, title = {Relational Summarization for Corpus Analysis}, url = {https://www.aclweb.org/anthology/N18-1159}, year = {2018}, bdsk-url-1 = {https://www.aclweb.org/anthology/N18-1159}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/N18-1159} }
NAACL
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses

Katherine Keith, Su Lin Blodgett, and Brendan O’Connor

In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018

Paper Bib

Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions. However, ambiguity is inherent to natural language syntax, and communicating such ambiguity is important for error analysis and better-informed downstream applications. In this work, we propose a transition sampling algorithm to sample from the full joint distribution of parse trees defined by a transition-based parsing model, and demonstrate the use of the samples in probabilistic dependency analysis. First, we define the new task of dependency path prediction, inferring syntactic substructures over part of a sentence, and provide the first analysis of performance on this task. Second, we demonstrate the usefulness of our Monte Carlo syntax marginal method for parser error analysis and calibration. Finally, we use this method to propagate parse uncertainty to two downstream information extraction applications: identifying persons killed by police and semantic role assignment.
@inproceedings{Keith2018MC, bibtex_show = {true}, abbr = {NAACL}, paper = {http://www.aclweb.org/anthology/N18-1084.pdf}, address = {New Orleans, Louisiana}, author = {Keith, Katherine and Blodgett, Su Lin and O'Connor, Brendan}, booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)}, date-added = {2018-07-18 14:28:51 +0000}, date-modified = {2018-07-18 14:29:08 +0000}, month = jun, pages = {917--928}, publisher = {Association for Computational Linguistics}, title = {Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses}, url = {http://www.aclweb.org/anthology/N18-1084}, year = {2018}, bdsk-url-1 = {http://www.aclweb.org/anthology/N18-1084} }

SCiL

Preface: SCiL 2018 Editors’ Note

Gaja Jarosz, Brendan O’Connor, and Joe Pater

Proceedings of the Society for Computation in Linguistics, 2018

Paper Bib

@article{Jarosz2018SCiL,
  bibtex_show = {true},
  abbr = {SCiL},
  paper = {https://doi.org/10.7275/R5GF0RQW},
  author = {Jarosz, Gaja and O'Connor, Brendan and Pater, Joe},
  date-added = {2018-11-14 04:37:47 +0000},
  date-modified = {2021-08-11 10:08:26 -0400},
  journal = {Proceedings of the Society for Computation in Linguistics},
  number = {1},
  title = {Preface: {SCiL} 2018 Editors' Note},
  volume = {1},
  year = {2018}
}

SIGIR

Exploring Diversification In Non-factoid Question Answering

Lakshmi Vikraman, W. Bruce Croft, and Brendan O’Connor

In Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval, 2018

Paper Bib

@inproceedings{Vikraman2018QA,
  bibtex_show = {true},
  abbr = {SIGIR},
  paper = {https://dl.acm.org/doi/pdf/10.1145/3234944.3234973},
  author = {Vikraman, Lakshmi and Croft, W. Bruce and O'Connor, Brendan},
  booktitle = {Proceedings of the 2018 {ACM} {SIGIR} International Conference on Theory of Information Retrieval},
  date-added = {2019-09-03 01:28:08 +0000},
  date-modified = {2021-08-11 10:03:39 -0400},
  title = {Exploring Diversification In Non-factoid Question Answering},
  year = {2018}
}

SIGIR

Understanding the Representational Power of Neural Retrieval Models Using NLP Tasks

Daniel Cohen, Brendan O’Connor, and W. Bruce Croft

In Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval, 2018

Paper Bib

@inproceedings{Cohen2018Interp,
  bibtex_show = {true},
  abbr = {SIGIR},
  paper = {https://dl.acm.org/doi/pdf/10.1145/3234944.3234959},
  author = {Cohen, Daniel and O'Connor, Brendan and Croft, W. Bruce},
  booktitle = {Proceedings of the 2018 {ACM} {SIGIR} International Conference on Theory of Information Retrieval},
  date-added = {2019-09-03 01:27:09 +0000},
  date-modified = {2019-09-05 06:24:36 +0000},
  title = {Understanding the Representational Power of Neural Retrieval Models Using NLP Tasks},
  year = {2018}
}

TextGraphs
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings

Haw-Shiuan Chang, Amol Agrawal, Ananya Ganesh, Anirudha Desai, Vinayak Mathur, Alfred Hough, and Andrew McCallum

In TextGraphs-12: the Workshop on Graph-based Methods for Natural Language Processing (NAACL WS), 2018

Paper Bib Slides
@inproceedings{conf/TextGraph18/Chang18, abbr = {TextGraphs}, bibtex_show = {true}, author = {Chang, Haw-Shiuan and Agrawal, Amol and Ganesh, Ananya and Desai, Anirudha and Mathur, Vinayak and Hough, Alfred and McCallum, Andrew}, title = {Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings}, booktitle = {TextGraphs-12: the Workshop on Graph-based Methods for Natural Language Processing (NAACL WS)}, year = {2018}, slides = {http://docs.wixstatic.com/ugd/e150d8_ae5222766cda446985cce83c1c72bae3.pdf}, paper = {https://arxiv.org/abs/1804.03257} }

2017

ACL
Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

Rajarshi Das, Manzil Zaheer, Siva Reddy, and Andrew McCallum

In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Vancouver, Canada, July 30 - August 4, Volume 2: Short Papers, 2017

Paper Bib
@inproceedings{DBLP:conf/acl/DasZRM17, abbr = {ACL}, bibtex_show = {true}, author = {Das, Rajarshi and Zaheer, Manzil and Reddy, Siva and McCallum, Andrew}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.org/rec/bib/conf/acl/DasZRM17}, booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics ({ACL}), Vancouver, Canada, July 30 - August 4, Volume 2: Short Papers}, doi = {10.18653/v1/P17-2057}, editor = {Barzilay, Regina and Kan, Min{-}Yen}, paper = {https://doi.org/10.18653/v1/P17-2057}, pages = {358--365}, publisher = {Association for Computational Linguistics}, timestamp = {Fri, 04 Aug 2017 16:38:24 +0200}, title = {Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks}, year = {2017} }
AKBC
RelNet: End-to-end Modeling of Entities & Relations

Trapit Bansal, Arvind Neelakantan, and Andrew McCallum

In 6th Workshop on Automated Knowledge Base Construction (AKBC) 2017 at NIPS, 2017

Paper Bib
@inproceedings{DBLP:journals/corr/BansalNM17, abbr = {AKBC}, bibtex_show = {true}, author = {Bansal, Trapit and Neelakantan, Arvind and McCallum, Andrew}, booktitle = {6th Workshop on Automated Knowledge Base Construction (AKBC) 2017 at NIPS}, paper = {http://arxiv.org/abs/1706.07179}, title = {RelNet: End-to-end Modeling of Entities {\&} Relations}, year = {2017} }

AKBC

Learning String Alignments for Entity Aliases

Aaron Traylor, Nicholas Monath, Rajarshi Das, and Andrew McCallum

In 6th Workshop on Automated Knowledge Base Construction (AKBC) 2017 at NIPS, 2017

Paper Bib Code

@inproceedings{conf/nips_ws/TraylorMDM17,
  title = {Learning String Alignments for Entity Aliases},
  abbr = {AKBC},
  bibtex_show = {true},
  author = {Traylor, Aaron and Monath, Nicholas and Das, Rajarshi and McCallum, Andrew},
  year = {2017},
  booktitle = {6th Workshop on Automated Knowledge Base Construction (AKBC) 2017 at NIPS},
  paper = {http://www.akbc.ws/2017/papers/28_paper.pdf},
  code = {https://github.com/iesl/learned-string-alignments}
}

AKBC
Entity-centric Attribute Feedback for Interactive Knowledge Bases

Ari Kobren, Nicholas Monath, and Andrew McCallum

In 6th Workshop on Automated Knowledge Base Construction (AKBC) 2017 at NIPS, 2017

Paper Bib
@inproceedings{conf/nips_ws/KobrenMM17, title = {Entity-centric Attribute Feedback for Interactive Knowledge Bases}, abbr = {AKBC}, bibtex_show = {true}, author = {Kobren, Ari and Monath, Nicholas and McCallum, Andrew}, year = {2017}, booktitle = {6th Workshop on Automated Knowledge Base Construction (AKBC) 2017 at NIPS}, paper = {http://www.akbc.ws/2017/papers/27_paper.pdf} }
DISCML
Gradient-based Hierarchical Clustering

Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, and Andrew McCallum

In NIPS Workshop on Discrete Structures in Machine Learning (DISCML) (Oral), 2017

Bib
@inproceedings{conf/nips_ws/MonathKKM17, title = {Gradient-based Hierarchical Clustering}, abbr = {DISCML}, bibtex_show = {true}, author = {Monath, Nicholas and Kobren, Ari and Krishnamurthy, Akshay and McCallum, Andrew}, year = {2017}, booktitle = {NIPS Workshop on Discrete Structures in Machine Learning (DISCML) (Oral)} }

DS+J

Rookie: summarization and visualization for news archives

Abram Handler and Brendan O’Connor

Data Science + Journalism Workshop (DS+J) at KDD, 2017

Paper Bib

@article{Handler2016RookieDSJ,
  bibtex_show = {true},
  abbr = {DS+J},
  paper = {https://arxiv.org/pdf/1708.01944.pdf},
  author = {Handler, Abram and O'Connor, Brendan},
  date-added = {2017-06-25 17:34:22 +0000},
  date-modified = {2017-06-25 17:35:14 +0000},
  journal = {Data Science + Journalism Workshop ({DS+J}) at {KDD}},
  title = {Rookie: summarization and visualization for news archives},
  year = {2017}
}

EACL

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

Patrick Verga, Arvind Neelakantan, and Andrew McCallum

In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Valencia, Spain, April 3-7, 2017, Volume 1: Long Papers (Oral), 2017

Paper Bib

@inproceedings{DBLP:conf/eacl/McCallumNV17,
  abbr = {EACL},
  bibtex_show = {true},
  author = {Verga, Patrick and Neelakantan, Arvind and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/eacl/McCallumNV17},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics ({EACL}), Valencia, Spain, April 3-7, 2017, Volume 1: Long Papers (Oral)},
  editor = {Lapata, Mirella and Blunsom, Phil and Koller, Alexander},
  paper = {http://aclanthology.info/papers/E17-1058/generalizing-to-unseen-entities-and-entity-pairs-with-row-less-universal-schema},
  pages = {613--622},
  publisher = {Association for Computational Linguistics},
  timestamp = {Wed, 09 Aug 2017 16:04:18 +0200},
  title = {Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema},
  year = {2017},
  data = {https://people.cs.umass.edu/~pat/data/EACL_rowless_entity_types.tar.gz}
}

EACL

Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum

In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Valencia, Spain, April 3-7, 2017, Volume 1: Long Papers, 2017

Paper Bib Slides

@inproceedings{DBLP:conf/eacl/McCallumNDB17,
  abbr = {EACL},
  bibtex_show = {true},
  author = {Das, Rajarshi and Neelakantan, Arvind and Belanger, David and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/eacl/McCallumNDB17},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics ({EACL}), Valencia, Spain, April 3-7, 2017, Volume 1: Long Papers},
  editor = {Lapata, Mirella and Blunsom, Phil and Koller, Alexander},
  paper = {http://www.aclweb.org/anthology/E17-1013},
  pages = {132--141},
  publisher = {Association for Computational Linguistics},
  timestamp = {Wed, 09 Aug 2017 16:04:18 +0200},
  slides = {http://rajarshd.github.io/talks/Chains_of_Reasoning.pdf},
  title = {Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks},
  year = {2017}
}

EMNLP

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum

In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, September 9-11, 2017, 2017

Paper Bib Code

@inproceedings{DBLP:conf/emnlp/StrubellVBM17,
  abbr = {EMNLP},
  bibtex_show = {true},
  author = {Strubell, Emma and Verga, Patrick and Belanger, David and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/emnlp/StrubellVBM17},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing ({EMNLP}), Copenhagen, Denmark, September 9-11, 2017},
  editor = {Palmer, Martha and Hwa, Rebecca and Riedel, Sebastian},
  paper = {https://arxiv.org/abs/1702.02098},
  pages = {2660--2670},
  publisher = {Association for Computational Linguistics},
  timestamp = {Fri, 15 Sep 2017 17:29:53 +0200},
  title = {Fast and Accurate Entity Recognition with Iterated Dilated Convolutions},
  year = {2017},
  code = {https://github.com/iesl/dilated-cnn-ner}
}

EMNLP
Identifying civilians killed by police with distantly supervised entity-event extraction

Katherine Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, and Brendan O’Connor

In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017

Paper Bib

We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases.
@inproceedings{Keith2017PF, bibtex_show = {true}, abbr = {EMNLP}, paper = {https://www.aclweb.org/anthology/D17-1163.pdf}, address = {Copenhagen, Denmark}, author = {Keith, Katherine and Handler, Abram and Pinkham, Michael and Magliozzi, Cara and McDuffie, Joshua and O{'}Connor, Brendan}, booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing}, date-added = {2019-09-03 01:58:34 +0000}, date-modified = {2019-09-03 01:58:43 +0000}, doi = {10.18653/v1/D17-1163}, month = sep, pages = {1547--1557}, publisher = {Association for Computational Linguistics}, title = {Identifying civilians killed by police with distantly supervised entity-event extraction}, url = {https://www.aclweb.org/anthology/D17-1163}, year = {2017}, bdsk-url-1 = {https://www.aclweb.org/anthology/D17-1163}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/D17-1163} }

EMNLP

The strange geometry of skip-gram with negative sampling

David Mimno and Laure Thompson

In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017

Paper Bib

@inproceedings{mimno-thompson-2017-strange,
  abbr = {EMNLP},
  bibtex_show = {true},
  title = {The strange geometry of skip-gram with negative sampling},
  author = {Mimno, David and Thompson, Laure},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month = sep,
  year = {2017},
  address = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  paper = {https://aclanthology.org/D17-1308},
  doi = {10.18653/v1/D17-1308},
  pages = {2873--2878}
}

FAT/ML

Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English

Su Lin Blodgett and Brendan O’Connor

arXiv preprint arXiv:1707.00061. Presented at Fairness, Accountability, and Transparency in Machine Learning workshop at KDD, 2017

Paper Bib

@article{Blodgett2017Disparity,
  bibtex_show = {true},
  abbr = {FAT/ML},
  paper = {https://arxiv.org/pdf/1707.00061.pdf},
  author = {Blodgett, Su Lin and O'Connor, Brendan},
  date-added = {2017-07-15 14:52:43 +0000},
  date-modified = {2019-06-01 03:58:57 +0000},
  journal = {arXiv preprint arXiv:1707.00061. Presented at Fairness, Accountability, and Transparency in Machine Learning workshop at KDD},
  publisher = {Presented at Fairness, Accountability, and Transparency in Machine Learning workshop at KDD 2017},
  title = {Racial Disparity in Natural Language Processing: A Case Study of Social Media {A}frican-{A}merican {E}nglish},
  year = {2017}
}

ICLR
Learning a Natural Language Interface with Neural Programmer

Arvind Neelakantan, Quoc V. Le, Martin Abadi, Andrew McCallum, and Dario Amodei

In International Conference on Learning Representations (ICLR), 2017

Paper Bib
@inproceedings{DBLP:conf/iclr/VilnisM16, abbr = {ICLR}, bibtex_show = {true}, author = {Neelakantan, Arvind and Le, Quoc V. and Abadi, Martin and McCallum, Andrew and Amodei, Dario}, title = {Learning a Natural Language Interface with Neural Programmer}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2017}, paper = {http://arxiv.org/abs/1611.08945} }

ICML

End-to-End Learning for Structured Prediction Energy Networks

David Belanger, Bishan Yang, and Andrew McCallum

In Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, NSW, Australia, 6-11 August 2017, 2017

Paper Bib

@inproceedings{DBLP:conf/icml/BelangerYM17,
  abbr = {ICML},
  bibtex_show = {true},
  author = {Belanger, David and Yang, Bishan and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/icml/BelangerYM17},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning ({ICML}), Sydney, NSW, Australia, 6-11 August 2017},
  editor = {Precup, Doina and Teh, Yee Whye},
  paper = {http://proceedings.mlr.press/v70/belanger17a.html},
  pages = {429--439},
  publisher = {PMLR},
  series = {Proceedings of Machine Learning Research},
  timestamp = {Wed, 16 Aug 2017 11:08:55 +0200},
  title = {End-to-End Learning for Structured Prediction Energy Networks},
  volume = {70},
  year = {2017}
}

ICML WS
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling

Dung Thai, Shikhar Murty, Trapit Bansal, Luke Vilnis, David Belanger, and Andrew McCallum

In International Conference on Machine Learning Workshop on Deep Structured Prediction (ICML WS), 2017

Paper Bib
@inproceedings{DBLP:conf/icml_ws/Thai17, abbr = {ICML WS}, bibtex_show = {true}, author = {Thai, Dung and Murty, Shikhar and Bansal, Trapit and Vilnis, Luke and Belanger, David and McCallum, Andrew}, title = {Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling}, booktitle = {International Conference on Machine Learning Workshop on Deep Structured Prediction (ICML WS)}, year = {2017}, paper = {http://arxiv.org/abs/1708.00553} }
ICML WS
Improved Representation Learning for Predicting Commonsense Ontologies

Xiang Li, Luke Vilnis, and Andrew McCallum

In International Conference on Machine Learning Workshop on Deep Structured Prediction (ICML WS), 2017

Paper Bib
@inproceedings{DBLP:conf/icml_ws/Li17, abbr = {ICML WS}, bibtex_show = {true}, author = {Li, Xiang and Vilnis, Luke and McCallum, Andrew}, title = {Improved Representation Learning for Predicting Commonsense Ontologies}, booktitle = {International Conference on Machine Learning Workshop on Deep Structured Prediction (ICML WS)}, year = {2017}, paper = {http://arxiv.org/abs/1708.00549} }
NIPS
Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples

Haw-Shiuan Chang, Erik G. Learned-Miller, and Andrew McCallum

In Advances in Neural Information Processing Systems (NIPS), 2017

Paper Bib Poster
@inproceedings{DBLP:conf/nips/ChangLM17, abbr = {NIPS}, bibtex_show = {true}, author = {Chang, Haw{-}Shiuan and Learned{-}Miller, Erik G. and McCallum, Andrew}, title = {Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples}, booktitle = {Advances in Neural Information Processing Systems (NIPS)}, year = {2017}, poster = {http://people.umass.edu/hawshiuancha/NIPS_poster_active_bias.pdf}, paper = {http://arxiv.org/abs/1704.07433} }
NIPS WS
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

Sheshera Mysore, Edward Kim, Emma Strubell, Ao Liu, Haw-Shiuan Chang, Srikrishna Kompella, Kevin Huang, Andrew McCallum, and Elsa Olivetti

In Workshop on Machine Learning for Molecules and Materials at NIPS, 2017

Paper Bib
@inproceedings{mysore2017automatically, title = {Automatically Extracting Action Graphs from Materials Science Synthesis Procedures}, abbr = {NIPS WS}, bibtex_show = {true}, author = {Mysore, Sheshera and Kim, Edward and Strubell, Emma and Liu, Ao and Chang, Haw-Shiuan and Kompella, Srikrishna and Huang, Kevin and McCallum, Andrew and Olivetti, Elsa}, booktitle = {Workshop on Machine Learning for Molecules and Materials at NIPS}, year = {2017}, paper = {https://arxiv.org/abs/1711.06872} }
NLP+CSS
Proceedings of the Second Workshop on NLP and Computational Social Science

Dirk Hovy, Svitlana Volkova, David Bamman, David Jurgens, Brendan O’Connor, Oren Tsur, and A. Seza Doğruöz

2017

Paper Bib
@proceedings{Hovy2017NLPCSS, bibtex_show = {true}, abbr = {NLP+CSS}, paper = {https://www.aclweb.org/anthology/W17-2900}, address = {Vancouver, Canada}, author = {Hovy, Dirk and Volkova, Svitlana and Bamman, David and Jurgens, David and O{'}Connor, Brendan and Tsur, Oren and Do{\u{g}}ru{\"o}z, A. Seza}, date-added = {2019-09-03 01:39:11 +0000}, date-modified = {2019-09-03 01:39:19 +0000}, doi = {10.18653/v1/W17-29}, month = aug, publisher = {Association for Computational Linguistics}, title = {Proceedings of the Second Workshop on {NLP} and Computational Social Science}, url = {https://www.aclweb.org/anthology/W17-2900}, year = {2017}, bdsk-url-1 = {https://www.aclweb.org/anthology/W17-2900}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/W17-29} }

SIGKDD

A Hierarchical Algorithm for Extreme Clustering

Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, and Andrew McCallum

In Proceedings of the 23rd ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Halifax, NS, Canada, August 13 - 17, 2017 (Oral), 2017

Paper Bib Code Talk

@inproceedings{DBLP:conf/kdd/KobrenMKM17,
  abbr = {SIGKDD},
  bibtex_show = {true},
  author = {Kobren, Ari and Monath, Nicholas and Krishnamurthy, Akshay and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/kdd/KobrenMKM17},
  booktitle = {Proceedings of the 23rd {ACM} International Conference on Knowledge Discovery and Data Mining ({SIGKDD}), Halifax, NS, Canada, August 13 - 17, 2017 (Oral)},
  doi = {10.1145/3097983.3098079},
  paper = {http://www.kdd.org/kdd2017/papers/view/an-online-hierarchical-algorithm-for-extreme-clustering},
  pages = {255--264},
  publisher = {ACM},
  timestamp = {Tue, 15 Aug 2017 16:10:36 +0200},
  title = {A Hierarchical Algorithm for Extreme Clustering},
  year = {2017},
  code = {https://github.com/iesl/xcluster},
  video = {http://videolectures.net/kdd2017_kobren_extreme_clustering/}
}

SPNLP

Dependency Parsing with Dilated Iterated Graph CNNs

Emma Strubell and Andrew McCallum

In Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing (SPNLP at EMNLP), Copenhagen, Denmark, September 2017, 2017

Paper Bib

@inproceedings{DBLP:conf/emnlp/StrubellM17,
  abbr = {SPNLP},
  bibtex_show = {true},
  author = {Strubell, Emma and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/emnlp/StrubellM17},
  booktitle = {Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing (SPNLP at EMNLP), Copenhagen, Denmark, September 2017},
  editor = {Chang, Kai{-}Wei and Chang, Ming{-}Wei and Srikumar, Vivek and Rush, Alexander M.},
  paper = {http://aclanthology.info/papers/W17-4301/w17-4301},
  pages = {1--6},
  publisher = {Association for Computational Linguistics},
  timestamp = {Mon, 18 Sep 2017 12:23:03 +0200},
  title = {Dependency Parsing with Dilated Iterated Graph CNNs},
  year = {2017}
}

SemEval
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, and Andrew McCallum

In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval at ACL), Vancouver, Canada, August 3-4, 2017, 2017

Paper Bib
@inproceedings{DBLP:conf/semeval/AugensteinDRVM17, abbr = {SemEval}, bibtex_show = {true}, author = {Augenstein, Isabelle and Das, Mrinal and Riedel, Sebastian and Vikraman, Lakshmi and McCallum, Andrew}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.org/rec/bib/conf/semeval/AugensteinDRVM17}, booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval at ACL), Vancouver, Canada, August 3-4, 2017}, doi = {10.18653/v1/S17-2091}, editor = {Bethard, Steven and Carpuat, Marine and Apidianaki, Marianna and Mohammad, Saif M. and Cer, Daniel M. and Jurgens, David}, paper = {http://www.aclweb.org/anthology/S17-2091}, pages = {546--555}, publisher = {Association for Computational Linguistics}, timestamp = {Wed, 16 Aug 2017 01:00:00 +0200}, title = {SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications}, year = {2017} }
WNUT
A Dataset and Classifier for Recognizing Social Media English

Su Lin Blodgett, Johnny Wei, and Brendan O’Connor

In Proceedings of the 3rd Workshop on Noisy User-generated Text, 2017

Paper Bib

While language identification works well on standard texts, it performs much worse on social media language, in particular dialectal language—even for English. First, to support work on English language identification, we contribute a new dataset of tweets annotated for English versus non-English, with attention to ambiguity, code-switching, and automatic generation issues. It is randomly sampled from all public messages, avoiding biases towards pre-existing language classifiers. Second, we find that a demographic language model—which identifies messages with language similar to that used by several U.S. ethnic populations on Twitter—can be used to improve English language identification performance when combined with a traditional supervised language identifier. It increases recall with almost no loss of precision, including, surprisingly, for English messages written by non-U.S. authors. Our dataset and identifier ensemble are available online.
@inproceedings{Blodgett2017LID, bibtex_show = {true}, abbr = {WNUT}, paper = {https://www.aclweb.org/anthology/W17-4408.pdf}, address = {Copenhagen, Denmark}, author = {Blodgett, Su Lin and Wei, Johnny and O{'}Connor, Brendan}, booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text}, date-added = {2019-09-03 01:56:08 +0000}, date-modified = {2019-09-03 01:56:15 +0000}, doi = {10.18653/v1/W17-4408}, month = sep, pages = {56--61}, publisher = {Association for Computational Linguistics}, title = {A Dataset and Classifier for Recognizing Social Media {E}nglish}, url = {https://www.aclweb.org/anthology/W17-4408}, year = {2017}, bdsk-url-1 = {https://www.aclweb.org/anthology/W17-4408}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/W17-4408} }
WWW
Learning to Extract Events from Knowledge Base Revisions

Alexander Konovalov, Benjamin Strauss, Alan Ritter, and Brendan O’Connor

In Proceedings of the 26th International Conference on World Wide Web, 2017

Paper Bib
@inproceedings{Konovalov2017Events, bibtex_show = {true}, abbr = {WWW}, paper = {http://brenocon.com/konovalov2017events.pdf}, author = {Konovalov, Alexander and Strauss, Benjamin and Ritter, Alan and O'Connor, Brendan}, booktitle = {Proceedings of the 26th International Conference on World Wide Web}, date-added = {2017-06-25 17:36:01 +0000}, date-modified = {2017-06-25 17:36:22 +0000}, organization = {International World Wide Web Conferences Steering Committee}, pages = {1007--1014}, title = {Learning to Extract Events from Knowledge Base Revisions}, year = {2017} }

2016

AKBC

Row-less Universal Schema

Patrick Verga and Andrew McCallum

In Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC at NAACL-HLT), San Diego, CA, USA, June 17, 2016 (Oral), 2016

Paper Bib

@inproceedings{DBLP:conf/akbc/VergaM16,
  abbr = {AKBC},
  bibtex_show = {true},
  author = {Verga, Patrick and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/akbc/VergaM16},
  booktitle = {Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC at NAACL-HLT), San Diego, CA, USA, June 17, 2016 (Oral)},
  editor = {Pujara, Jay and Rockt{\"{a}}schel, Tim and Chen, Danqi and Singh, Sameer},
  paper = {http://aclweb.org/anthology/W/W16/W16-1312.pdf},
  pages = {63--68},
  publisher = {The Association for Computer Linguistics},
  timestamp = {Mon, 19 Sep 2016 17:23:19 +0200},
  title = {Row-less Universal Schema},
  year = {2016}
}

AKBC

Incorporating Selectional Preferences in Multi-hop Relation Extraction

Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum

In Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC at NAACL-HLT), San Diego, CA, USA, June 17, 2016, 2016

Paper Bib

@inproceedings{DBLP:conf/akbc/DasNBM16,
  abbr = {AKBC},
  bibtex_show = {true},
  author = {Das, Rajarshi and Neelakantan, Arvind and Belanger, David and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/akbc/DasNBM16},
  booktitle = {Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC at NAACL-HLT), San Diego, CA, USA, June 17, 2016},
  editor = {Pujara, Jay and Rockt{\"{a}}schel, Tim and Chen, Danqi and Singh, Sameer},
  paper = {http://aclweb.org/anthology/W/W16/W16-1304.pdf},
  pages = {18--23},
  publisher = {The Association for Computer Linguistics},
  timestamp = {Mon, 19 Sep 2016 17:23:19 +0200},
  title = {Incorporating Selectional Preferences in Multi-hop Relation Extraction},
  year = {2016}
}

AKBC
Call for Discussion: Building a New Standard Dataset for Relation Extraction Tasks

Teresa Martin, Fiete Botschen, Ajay Nagesh, and Andrew McCallum

In Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC at NAACL-HLT), San Diego, CA, USA, June 17, 2016, 2016

Paper Bib
@inproceedings{DBLP:conf/akbc/MartinBNM16, abbr = {AKBC}, bibtex_show = {true}, author = {Martin, Teresa and Botschen, Fiete and Nagesh, Ajay and McCallum, Andrew}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.org/rec/bib/conf/akbc/MartinBNM16}, booktitle = {Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC at NAACL-HLT), San Diego, CA, USA, June 17, 2016}, editor = {Pujara, Jay and Rockt{\"{a}}schel, Tim and Chen, Danqi and Singh, Sameer}, paper = {http://aclweb.org/anthology/W/W16/W16-1317.pdf}, pages = {92--96}, publisher = {The Association for Computer Linguistics}, timestamp = {Mon, 19 Sep 2016 17:23:19 +0200}, title = {Call for Discussion: Building a New Standard Dataset for Relation Extraction Tasks}, year = {2016} }

CIKM

Improving Entity Ranking for Keyword Queries

John Foley, Brendan O’Connor, and James Allan

In Proceedings of CIKM, 2016

Paper Bib

@inproceedings{Foley2016Ranking,
  bibtex_show = {true},
  abbr = {CIKM},
  paper = {http://maroo.cs.umass.edu/getpdf.php?id=1223},
  author = {Foley, John and O'Connor, Brendan and Allan, James},
  booktitle = {Proceedings of {CIKM}},
  date-added = {2016-08-29 14:32:10 +0000},
  date-modified = {2016-08-29 21:02:55 +0000},
  title = {Improving Entity Ranking for Keyword Queries},
  year = {2016}
}

EMNLP

Demographic Dialectal Variation in Social Media: A Case Study of African-American English

Su Lin Blodgett, Lisa Green, and Brendan O’Connor

Proceedings of EMNLP, 2016

Paper Bib

@article{Blodgett2016AAE,
  bibtex_show = {true},
  abbr = {EMNLP},
  paper = {https://aclanthology.org/D16-1120.pdf},
  author = {Blodgett, Su Lin and Green, Lisa and O'Connor, Brendan},
  date-added = {2016-07-19 02:19:28 +0000},
  date-modified = {2016-07-31 16:48:45 +0000},
  journal = {Proceedings of {EMNLP}},
  title = {Demographic Dialectal Variation in Social Media: A Case Study of {A}frican-{A}merican {E}nglish},
  year = {2016}
}

ICML

Structured Prediction Energy Networks

David Belanger and Andrew McCallum

In Proceedings of the 33rd International Conference on Machine Learning (ICML), New York City, NY, USA, June 19-24, 2016, 2016

Paper Bib

@inproceedings{DBLP:conf/icml/BelangerM16,
  abbr = {ICML},
  bibtex_show = {true},
  author = {Belanger, David and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/icml/BelangerM16},
  booktitle = {Proceedings of the 33rd International Conference on Machine Learning ({ICML}), New York City, NY, USA, June 19-24, 2016},
  editor = {Balcan, Maria{-}Florina and Weinberger, Kilian Q.},
  paper = {http://jmlr.org/proceedings/papers/v48/belanger16.html},
  pages = {983--992},
  publisher = {JMLR.org},
  series = {{JMLR} Workshop and Conference Proceedings},
  timestamp = {Tue, 12 Jul 2016 21:51:16 +0200},
  title = {Structured Prediction Energy Networks},
  volume = {48},
  year = {2016}
}

NAACL

Multilingual Relation Extraction using Compositional Universal Schema

Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, and Andrew McCallum

In NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT/NAACL), San Diego California, USA, June 12-17, 2016 (Oral), 2016

Paper Bib Code

@inproceedings{DBLP:conf/naacl/VergaBSRM16,
  abbr = {NAACL},
  bibtex_show = {true},
  author = {Verga, Patrick and Belanger, David and Strubell, Emma and Roth, Benjamin and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/naacl/VergaBSRM16},
  booktitle = {{NAACL} {HLT} 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT/NAACL), San Diego California, USA, June 12-17, 2016 (Oral)},
  editor = {Knight, Kevin and Nenkova, Ani and Rambow, Owen},
  paper = {http://aclweb.org/anthology/N/N16/N16-1103.pdf},
  pages = {886--896},
  publisher = {The Association for Computational Linguistics},
  timestamp = {Tue, 13 Sep 2016 19:52:39 +0200},
  title = {Multilingual Relation Extraction using Compositional Universal Schema},
  year = {2016},
  code = {https://github.com/patverga/torch-relation-extraction},
  data = {https://people.cs.umass.edu/~pat/data/naacl-data.tar.gz}
}

NLP+CSS
Proceedings of the First Workshop on NLP and Computational Social Science

David Bamman, A. Seza Doğruöz, Jacob Eisenstein, Dirk Hovy, David Jurgens, Brendan O’Connor, Alice Oh, Oren Tsur, and Svitlana Volkova

2016

Paper Bib
@proceedings{Hovy2016NLPCSS, bibtex_show = {true}, abbr = {NLP+CSS}, paper = {https://www.aclweb.org/anthology/W16-5600}, address = {Austin, Texas}, author = {Bamman, David and Do{\u{g}}ru{\"o}z, A. Seza and Eisenstein, Jacob and Hovy, Dirk and Jurgens, David and O{'}Connor, Brendan and Oh, Alice and Tsur, Oren and Volkova, Svitlana}, date-added = {2019-09-03 01:39:27 +0000}, date-modified = {2019-09-03 01:39:35 +0000}, doi = {10.18653/v1/W16-56}, month = nov, publisher = {Association for Computational Linguistics}, title = {Proceedings of the First Workshop on {NLP} and Computational Social Science}, url = {https://www.aclweb.org/anthology/W16-5600}, year = {2016}, bdsk-url-1 = {https://www.aclweb.org/anthology/W16-5600}, bdsk-url-2 = {http://dx.doi.org/10.18653/v1/W16-56} }

NLP+CSS

Bag of what? Simple noun phrase extraction for corpus analysis

Abram Handler, Matt Denny, Hanna Wallach, and Brendan O’Connor

In Proceedings of EMNLP: NLP+CSS: Workshop in Natural Language Processing and Computational Social Science, 2016

Paper Bib

@inproceedings{Handler2016Phrases,
  bibtex_show = {true},
  abbr = {NLP+CSS},
  paper = {https://aclanthology.org/W16-5615.pdf},
  author = {Handler, Abram and Denny, Matt and Wallach, Hanna and O'Connor, Brendan},
  booktitle = {Proceedings of EMNLP: NLP+CSS: Workshop in Natural Language Processing and Computational Social Science},
  date-added = {2016-09-21 17:42:40 +0000},
  date-modified = {2016-09-21 17:45:11 +0000},
  title = {Bag of what? Simple noun phrase extraction for corpus analysis},
  year = {2016}
}

RecSys

Ask the GRU: Multi-task Learning for Deep Text Recommendations

Trapit Bansal, David Belanger, and Andrew McCallum

In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), Boston, MA, USA, September 15-19, 2016, 2016

Paper Bib

@inproceedings{DBLP:conf/recsys/BansalBM16,
  abbr = {RecSys},
  bibtex_show = {true},
  author = {Bansal, Trapit and Belanger, David and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/recsys/BansalBM16},
  booktitle = {Proceedings of the 10th {ACM} Conference on Recommender Systems (RecSys), Boston, MA, USA, September 15-19, 2016},
  doi = {10.1145/2959100.2959180},
  editor = {Sen, Shilad and Geyer, Werner and Freyne, Jill and Castells, Pablo},
  paper = {http://doi.acm.org/10.1145/2959100.2959180},
  pages = {107--114},
  publisher = {ACM},
  timestamp = {Wed, 07 Sep 2016 13:42:11 +0200},
  title = {Ask the {GRU}: Multi-task Learning for Deep Text Recommendations},
  year = {2016}
}

TAC/KBP
Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema

Haw-Shiuan Chang, Abdurrahman Munir, Ao Liu, Johnny Tian-Zheng Wei, Aaron Traylor, Ajay Nagesh, Nicholas Monath, Patrick Verga, Emma Strubell, and Andrew McCallum

In Text Analysis Conference, Knowledge Base Population (TAC/KBP), 2016

Paper Bib
@inproceedings{DBLP:conf/tac/Chang16, abbr = {TAC/KBP}, bibtex_show = {true}, author = {Chang, Haw-Shiuan and Munir, Abdurrahman and Liu, Ao and Wei, Johnny Tian-Zheng and Traylor, Aaron and Nagesh, Ajay and Monath, Nicholas and Verga, Patrick and Strubell, Emma and McCallum, Andrew}, title = {Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema}, booktitle = {Text Analysis Conference, Knowledge Base Population (TAC/KBP)}, year = {2016}, paper = {https://pdfs.semanticscholar.org/e53e/b683d8380479a8977d4aef0048e26981cdbe.pdf} }

WHI

Visualizing textual models with in-text and word-as-pixel highlighting

Abram Handler, Su Lin Blodgett, and Brendan O’Connor

arXiv:1606.06352 at Workshop on Human Interpretability in Machine Learning, 2016

Paper Bib

@article{Handler2016Visualizing,
  bibtex_show = {true},
  abbr = {WHI},
  paper = {https://arxiv.org/pdf/1606.06352.pdf},
  author = {Handler, Abram and Blodgett, Su Lin and O'Connor, Brendan},
  date-added = {2019-09-05 04:43:04 +0000},
  date-modified = {2019-09-05 04:43:44 +0000},
  journal = {arXiv:1606.06352 at Workshop on Human Interpretability in Machine Learning},
  title = {Visualizing textual models with in-text and word-as-pixel highlighting},
  year = {2016}
}

2015

AAAI-SS
Compositional Vector Space Models for Knowledge Base Inference

Arvind Neelakantan, Benjamin Roth, and Andrew McCallum

In AAAI Spring Symposium Series (AAAI-SS), 2015

Paper Bib
@inproceedings{DBLP:conf/aaai-ss/Neelakantan15, abbr = {AAAI-SS}, bibtex_show = {true}, author = {Neelakantan, Arvind and Roth, Benjamin and McCallum, Andrew}, title = {Compositional Vector Space Models for Knowledge Base Inference}, booktitle = {AAAI Spring Symposium Series (AAAI-SS)}, year = {2015}, paper = {https://www.aaai.org/ocs/index.php/SSS/SSS15/paper/viewFile/10254/10032} }
AAAI-SS
Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches

Evgeniy Gabrilovich, Ramanathan Guha, Andrew McCallum, and Kevin Murphy

In AAAI Spring Symposium Series (AAAI-SS), 2015

Paper Bib
@inproceedings{DBLP:conf/aaai-ss/Benjamin14, abbr = {AAAI-SS}, bibtex_show = {true}, author = {Gabrilovich, Evgeniy and Guha, Ramanathan and McCallum, Andrew and Murphy, Kevin}, title = {Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches}, booktitle = {AAAI Spring Symposium Series (AAAI-SS)}, year = {2015}, paper = {http://www.aaai.org/Press/Reports/Symposia/Spring/ss-15-03.php} }

ACL

Compositional Vector Space Models for Knowledge Base Completion

Arvind Neelakantan, Benjamin Roth, and Andrew McCallum

In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL), July 26-31, 2015, Beijing, China, Volume 1: Long Papers, 2015

Paper Bib

@inproceedings{DBLP:conf/acl/NeelakantanRM15,
  abbr = {ACL},
  bibtex_show = {true},
  author = {Neelakantan, Arvind and Roth, Benjamin and McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/acl/NeelakantanRM15},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing ({ACL}), July 26-31, 2015, Beijing, China, Volume 1: Long Papers},
  paper = {http://aclweb.org/anthology/P/P15/P15-1016.pdf},
  pages = {156--166},
  publisher = {The Association for Computer Linguistics},
  timestamp = {Sun, 02 Aug 2015 19:10:39 +0200},
  title = {Compositional Vector Space Models for Knowledge Base Completion},
  year = {2015}
}

ACL
Learning Dynamic Feature Selection for Fast Sequential Prediction

Emma Strubell, Luke Vilnis, Kate Silverstein, and Andrew McCallum

In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL), July 26-31, 2015, Beijing, China, Volume 1: Long Papers (Outstanding Paper Award), 2015

Paper Bib
@inproceedings{DBLP:conf/acl/StrubellVSM15, abbr = {ACL}, bibtex_show = {true}, author = {Strubell, Emma and Vilnis, Luke and Silverstein, Kate and McCallum, Andrew}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {http://dblp.org/rec/bib/conf/acl/StrubellVSM15}, booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing ({ACL}), July 26-31, 2015, Beijing, China, Volume 1: Long Papers (Outstanding Paper Award)}, paper = {http://aclweb.org/anthology/P/P15/P15-1015.pdf}, pages = {146--155}, publisher = {The Association for Computer Linguistics}, timestamp = {Sun, 02 Aug 2015 19:10:39 +0200}, title = {Learning Dynamic Feature Selection for Fast Sequential Prediction}, year = {2015} }

EMNLP

Posterior calibration and exploratory analysis for natural language processing models

Khanh Nguyen and Brendan O’Connor

In Proceedings of EMNLP, 2015

Paper Bib

@inproceedings{Nguyen2015Calib,
  bibtex_show = {true},
  abbr = {EMNLP},
  paper = {https://aclanthology.org/D15-1182.bib},
  address = {Lisbon, Portugal},
  author = {Nguyen, Khanh and O'Connor, Brendan},
  booktitle = {Proceedings of {EMNLP}},
  date-added = {2015-09-28 01:46:41 +0000},
  date-modified = {2017-07-17 23:42:58 +0000},
  keywords = {mypapers},
  long_booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  month = sep,
  pages = {1587--1598},
  publisher = {Association for Computational Linguistics},
  title = {Posterior calibration and exploratory analysis for natural language processing models},
  url = {https://aclanthology.org/D15-1182},
  year = {2015},
  bdsk-url-1 = {http://aclweb.org/anthology/D15-1182}
}

ICLR
Word Representations via Gaussian Embedding

Luke Vilnis and Andrew McCallum

In International Conference on Learning Representations (ICLR) (Oral), 2015

Paper Bib
@inproceedings{DBLP:conf/iclr/VilnisM15, abbr = {ICLR}, bibtex_show = {true}, author = {Vilnis, Luke and McCallum, Andrew}, title = {Word Representations via Gaussian Embedding}, booktitle = {International Conference on Learning Representations (ICLR) (Oral)}, year = {2015}, paper = {http://arxiv.org/abs/1412.6623} }

ICTIR

Embedded Representations of Lexical and Knowledge-Base Semantics

Andrew McCallum

In Proceedings of the 2015 International Conference on The Theory of Information Retrieval (ICTIR), Northampton, Massachusetts, USA, September 27-30, 2015, 2015

Paper Bib

@inproceedings{DBLP:conf/ictir/McCallum15,
  abbr = {ICTIR},
  bibtex_show = {true},
  author = {McCallum, Andrew},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl = {http://dblp.org/rec/bib/conf/ictir/McCallum15},
  booktitle = {Proceedings of the 2015 International Conference on The Theory of Information Retrieval ({ICTIR}), Northampton, Massachusetts, USA, September 27-30, 2015},
  doi = {10.1145/2808194.2808195},
  editor = {Allan, James and Croft, W. Bruce and de Vries, Arjen P. and Zhai, Chengxiang},
  paper = {http://doi.acm.org/10.1145/2808194.2808195},
  pages = {1},
  publisher = {ACM},
  timestamp = {Tue, 03 Nov 2015 14:42:32 +0100},
  title = {Embedded Representations of Lexical and Knowledge-Base Semantics},
  year = {2015}
}

MPSA
A Little Bit of NLP Goes A Long Way: Finding Meaning in Legislative Texts with Phrase Extraction

Matthew J. Denny, Brendan O’Connor, and Hanna Wallach

Midwest Political Science Association (MPSA) 73rd Annual Conference, Chicago (IL), 2015

Bib
@article{Denny2015MPSA, bibtex_show = {true}, abbr = {MPSA}, author = {Denny, Matthew J. and O'Connor, Brendan and Wallach, Hanna}, date-added = {2016-08-30 14:13:29 +0000}, date-modified = {2019-09-04 17:58:03 +0000}, journal = {Midwest Political Science Association (MPSA) 73rd Annual Conference, Chicago (IL)}, title = {A Little Bit of NLP Goes A Long Way: Finding Meaning in Legislative Texts with Phrase Extraction}, year = {2015} }

TAC/KBP

Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches

Benjamin Roth, Nicholas Monath, David Belanger, Emma Strubell, Patrick Verga, and Andrew McCallum

In Text Analysis Conference, Knowledge Base Population (TAC/KBP), 2015

Paper Bib

@inproceedings{DBLP:conf/tac/Benjamin15,
  abbr = {TAC/KBP},
  bibtex_show = {true},
  author = {Roth, Benjamin and Monath, Nicholas and Belanger, David and Strubell, Emma and Verga, Patrick and McCallum, Andrew},
  title = {Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches},
  booktitle = {Text Analysis Conference, Knowledge Base Population (TAC/KBP)},
  year = {2015},
  paper = {https://tac.nist.gov/publications/2015/participant.papers/TAC2015.UMass_IESL.proceedings.pdf}
}