Assistive Technology ------------------------- Sonya S. Nikolova, Jordan Boyd-Graber, and Christiane Fellbaum. Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools. Modeling, Learning and Processing of Text Technological Data Structures, 2011. http://umiacs.umd.edu/~jbg/docs/2011_book_chapter_evocation.pdf Sonya S. Nikolova, Jordan Boyd-Graber, Christiane Fellbaum, and Perry Cook. Better Vocabularies for Assistive Communication Aids: Connecting Terms using Semantic Networks and Untrained Annotators. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/evocation-viva.pdf Xiaojuan Ma, Jordan Boyd-Graber, Sonya S. Nikolova, and Perry Cook. Speaking Through Pictures: Images vs. Icons. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/image_icon.pdf Jordan Boyd-Graber, Sonya S. Nikolova, Karyn A. Moffatt, Kenrick C. Kin, Joshua Y. Lee, Lester W. Mackey, Marilyn M. Tremaine, and Maria M. Klawe. Participatory design with proxies: Developing a desktop-PDA system to support people with aphasia. Computer-Human Interaction, 2006. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/paper673-boyd-graber.pdf Bayesian Non-parametrics ------------------------- Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah Cai, Jennifer Midberry, and Yuanxin Wang. Modeling Topic Control to Detect Influence in Conversations using Nonparametric Topic Models. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_influencer.pdf Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. http://umiacs.umd.edu/~jbg/docs/2014_tacl_ag_vb_online.pdf Ke Zhai and Jordan Boyd-Graber. Online Topic Models with Infinite Vocabulary. International Conference on Machine Learning, 2013. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_icml_infvoc.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Stephen Altschul. Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein Space. Journal of Computational Biology, 2013. http://umiacs.umd.edu/~jbg/docs/2013_dp_protein.pdf Yuening Hu, Jordan Boyd-Graber, Hal Daume III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_coalescent.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_shlda.pdf Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. (50% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_argviz.pdf Naho Orita, Rebecca McKeown, Naomi H. Feldman, Jeffrey Lidz, and Jordan Boyd-Graber. Discovering Pronoun Categories using Discourse Information. Proceedings of the Cognitive Science Society, 2013. http://umiacs.umd.edu/~jbg/docs/2013_cogsci_pronoun.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations. Association for Computational Linguistics, 2012. (19% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference of Machine Learning, 2012. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mtibp_icml_2012.pdf Yuening Hu and Jordan Boyd-Graber. Bayesian Hierarchical Clustering with Beta Coalescents. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Ke Zhai and Jordan Boyd-Graber. Online Topic Model with Infinite Vocabulary. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. "I Want to Talk About, Again, My Record On Energy …'': Modeling Topic Control in Conversations using Speaker-centric Nonparametric Topic Models. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Eric Hardisty, Jordan Boyd-Graber, and Philip Resnik. Modeling Perspective using Adaptor Grammars. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/adapted_naive_bayes.pdf Jordan Boyd-Graber and David M. Blei. Syntactic Topic Models. Neural Information Processing Systems, 2008. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2008.pdf Computational Biology ------------------------- Viet-An Nguyen, Jordan Boyd-Graber, and Stephen Altschul. Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein Space. Journal of Computational Biology, 2013. http://umiacs.umd.edu/~jbg/docs/2013_dp_protein.pdf Yuening Hu, Jordan Boyd-Graber, Hal Daume III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_coalescent.pdf Data Mining ------------------------- Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Multilingual Topic Model for Learning Weighted Topic Links Across Incomparable Corpora. Empirical Methods in Natural Language Processing, 2019. http://umiacs.umd.edu/~jbg/docs/2019_emnlp_mtm.pdf Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei, and James A. Evans. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academies of Science, 2018. Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_tree_prior.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_docblock.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2016. Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors. Empirical Methods in Natural Language Processing, 2015. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_hinge_link.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Viet-An Nguyen, Jordan Boyd-Graber, Jonathan Chang, and Philip Resnik. Tree-Based Label Dependency Topic Models. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Ke Zhai, Jordan Boyd-Graber, Nima Asadi, and Mohamad Alkhouja. Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce. ACM International Conference on World Wide Web, 2012. (12% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mrlda.pdf Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/itm.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Connections between the Lines: Augmenting Social Networks with Text. Knowledge Discovery and Data Mining, 2009. (9% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/kdd2009.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Discovering social networks from free text. 3rd Annual Machine Learning Symposium, 2008. Deep Learning ------------------------- Benjamin Börschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, and Lierni Sestorain Saralegu. Meta Answering for Machine Reading. ArXiv, Preprint. http://umiacs.umd.edu/~jbg/docs/2020_acl_refine.pdf Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. http://umiacs.umd.edu/~jbg/https://arxiv.org/abs/1904.04792 Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul, and Jordan Boyd-Graber. Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries. Association for Computational Linguistics, 2020. (17.6% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_acl_refine.pdf Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. Association for the Advancement of Artificial Intelligence, 2020. (20.6% Acceptance Rate) http://umiacs.umd.edu/~jbg/https://arxiv.org/abs/1812.09617 Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater. Which Evaluations Uncover Sense Representations that Actually Make Sense?. Linguistic Resources and Evaluation Conference, 2020. http://umiacs.umd.edu/~jbg/docs/2020_lrec_sense.pdf Chen Zhao, Chenyan Xiong, Xin Qian, and Jordan Boyd-Graber. Complex Factoid Question Answering with a Free-Text Knowledge Graph. The Web Conference, 2020. (19.2% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_www_delft.pdf Yoshinari Fujinuma, Michael Paul, and Jordan Boyd-Graber. A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity. Association for Computational Linguistics, 2019. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_modularity.pdf Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, and Jordan Boyd-Graber. Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. Association for Computational Linguistics, 2019. (18.3% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_clwe.pdf Eric Wallace, Shi Feng, and Jordan Boyd-Graber. Misleading Failures of Partial-input Baselines. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_flipside.pdf Ahmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. Can You Unpack That? Learning to Rewrite Questions-in-Context. Empirical Methods in Natural Language Processing, 2019. http://umiacs.umd.edu/~jbg/docs/2019_emnlp_sequentialqa.pdf Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. ACL Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing, 2018. Shi Feng, Eric Wallace, and Jordan Boyd-Graber. Interpreting Neural Networks with Nearest Neighbors. EMNLP Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2018. http://umiacs.umd.edu/~jbg/http://aclweb.org/anthology/W18-5416 Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. Dataset and Baselines for Sequential Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2018. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_linked.pdf Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_naacl_colorization.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Hadi Amiri, Philip Resnik, Jordan Boyd-Graber, and Hal Daume III. Learning Text Pair Similarity with Context-sensitive Autoencoders. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_context_ae.pdf Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daume III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015.This won the best demonstration award at NIPS 2015 Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_rnn_ideology.pdf Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daume III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_qb_rnn.pdf Mohit Iyyer, Jordan Boyd-Graber, and Hal Daume III. Generating Sentences from Semantic Vector Space Representations. NIPS Workshop on Learning Semantics, 2014. Digital Humanities ------------------------- Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf Clay Templeton, Travis Brown, Sayan Battacharyya, and Jordan Boyd-Graber. Mining the Dispatch under Supervision: Using Casualty Counts to Guide Topics from the Richmond Daily Dispatch Corpus. Chicago Colloquium on Digital Humanities and Computer Science, 2011. http://umiacs.umd.edu/~jbg/docs/slda_civil_war.pdf Empirical Human Data Collection ------------------------- Benjamin Börschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, and Lierni Sestorain Saralegu. Meta Answering for Machine Reading. ArXiv, Preprint. http://umiacs.umd.edu/~jbg/docs/2020_acl_refine.pdf Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. http://umiacs.umd.edu/~jbg/https://arxiv.org/abs/1904.04792 Denis Peskov, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, and Jordan Boyd-Graber. It Takes Two to Lie: One to Lie and One to Listen. Association for Computational Linguistics, 2020. (25.4% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_acl_diplomacy.pdf Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater. Which Evaluations Uncover Sense Representations that Actually Make Sense?. Linguistic Resources and Evaluation Conference, 2020. http://umiacs.umd.edu/~jbg/docs/2020_lrec_sense.pdf Ahmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. Can You Unpack That? Learning to Rewrite Questions-in-Context. Empirical Methods in Natural Language Processing, 2019. http://umiacs.umd.edu/~jbg/docs/2019_emnlp_sequentialqa.pdf Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_iui_augment.pdf Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Transactions of the Association of Computational Linguistics, 2019. http://umiacs.umd.edu/~jbg/docs/2019_tacl_trick.pdf Eric Wallace and Jordan Boyd-Graber. Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. ACL Student Research Workshop, 2018. http://umiacs.umd.edu/~jbg/http://aclweb.org/anthology/P18-3018 Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. Dataset and Baselines for Sequential Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2018. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_linked.pdf Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Paul Felt, Eric Ringger, Kevin Seppi, and Jordan Boyd-Graber. Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types. International Conference on Computational Linguistics, 2018. (37% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_coling_measurements.pdf Shudong Hao, Michael J. Paul, and Jordan Boyd-Graber. Lessons from the Bible on Modern Topics: Multilingual Topic Model Evaluation on Low-Resource Languages. North American Association for Computational Linguistics, 2018. (35% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_naacl_mltm_eval.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_naacl_colorization.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alvin Grissom II, Naho Orita, and Jordan Boyd-Graber. Incremental Prediction of Sentence-final Verbs. Conference on Computational Natural Language Learning, 2016. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_conll_verbpred.pdf He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_icml_opponent.pdf Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016. http://umiacs.umd.edu/~jbg/docs/2016_naacl_paintings.pdf Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf He He, Jordan Boyd-Graber, and Hal Daume III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_interpretese.pdf Vlad Niculae, Srijan Kumar, Jordan Boyd-Graber, and Cristian Danescu-Niculescu-Mizil. Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_diplomacy.pdf Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. This paper received the best paper award at CoNLL (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_conll_cslda.pdf Anupam Guha, Mohit Iyyer, Danny Bouman, and Jordan Boyd-Graber. Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_qb_coref.pdf Jordan Boyd-Graber, David Mimno, and David Newman. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. Handbook of Mixed Membership Models and Their Applications, 2014. http://umiacs.umd.edu/~jbg/docs/2014_book_chapter_care_and_feeding.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf Yuening Hu and Jordan Boyd-Graber. Suggesting Constraints for Interactive Topic Modeling. ICML Workshop on Machine Learning in Human Computation and Crowdsourcing, 2012. Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment. IEEE International Conference on Social Computing, 2011. (10% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/simulating_audiences.pdf Sonya S. Nikolova, Jordan Boyd-Graber, and Christiane Fellbaum. Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools. Modeling, Learning and Processing of Text Technological Data Structures, 2011. http://umiacs.umd.edu/~jbg/docs/2011_book_chapter_evocation.pdf Jordan Boyd-Graber. Linguistic Resource Creation in a Web 2.0 World. NSF Workshop on Collaborative Annotation, 2011. http://umiacs.umd.edu/~jbg/docs/2011_resources.pdf Brianna Satinoff and Jordan Boyd-Graber. Trivial Classification: What features do humans use for classification?. Workshop on Crowdsourcing Technologies for Language and Cognition Studies, 2011. Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Comparing Values and Sentiment Using Mechanical Turk. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-comparing.pdf Kenneth R. Fleischmann, Clay Templeton, and Jordan Boyd-Graber. Modeling Diverse Standpoints in Text Classification: Learning to Be Human by Modeling Human Values. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-learning.pdf Nitin Madnani, Jordan Boyd-Graber, and Philip Resnik. Measuring Transitivity Using Untrained Annotators. Creating Speech and Language Data With Amazon's Mechanical Turk, 2010. http://umiacs.umd.edu/~jbg/docs/madnani-boyd-graber-turk-workshop.pdf Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems, 2009. Jonathan Chang and I shared a NIPS student award honorable mention for this paper (5 out of 1105) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Jordan Boyd-Graber, Christiane Fellbaum, Daniel Osherson, and Robert Schapire. Adding Dense, Weighted, Connections to WordNet. Proceedings of the Global WordNet Conference, 2006. http://umiacs.umd.edu/~jbg/docs/jbg-jeju.pdf Human-Computer Interaction ------------------------- Alison Smith, Jordan Boyd-Graber, Ron Fan, Melissa Birchfield, Tongshuang Wu, Dan Weld, and Leah Findlater. No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML. Computer-Human Interaction, 2020. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_chi_explanation.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Digging into User Control: Perceptions of Adherence and Instability in Transparent Models. Intelligent User Interfaces, 2020. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_iui_control.pdf Varun Kumar, Alison Smith, Leah Findlater, Kevin Seppi, and Jordan Boyd-Graber. Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_control.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018.Alison won a best student paper honorable mention (3 out of 300) (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_iui_itm.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. http://umiacs.umd.edu/~jbg/http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Jordan Boyd-Graber. Humans and Computers Working Together to Measure Machine Learning Interpretability. The Bridge, 2017. Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, and Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_doclabel.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_icml_opponent.pdf Forough Poursabzi-Sangdeh and Jordan Boyd-Graber. Speeding Document Annotation with Topic Models. NAACL Student Research Workshop, 2015. Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_itm.pdf Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeffrey Heer. Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations. NIPS Workshop on Human-Propelled Machine Learning, 2014. Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. (50% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_argviz.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf Yuening Hu and Jordan Boyd-Graber. Suggesting Constraints for Interactive Topic Modeling. ICML Workshop on Machine Learning in Human Computation and Crowdsourcing, 2012. Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/itm.pdf Brianna Satinoff and Jordan Boyd-Graber. Trivial Classification: What features do humans use for classification?. Workshop on Crowdsourcing Technologies for Language and Cognition Studies, 2011. Sonya S. Nikolova, Jordan Boyd-Graber, Christiane Fellbaum, and Perry Cook. Better Vocabularies for Assistive Communication Aids: Connecting Terms using Semantic Networks and Untrained Annotators. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/evocation-viva.pdf Xiaojuan Ma, Jordan Boyd-Graber, Sonya S. Nikolova, and Perry Cook. Speaking Through Pictures: Images vs. Icons. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/image_icon.pdf Jordan Boyd-Graber, Sonya S. Nikolova, Karyn A. Moffatt, Kenrick C. Kin, Joshua Y. Lee, Lester W. Mackey, Marilyn M. Tremaine, and Maria M. Klawe. Participatory design with proxies: Developing a desktop-PDA system to support people with aphasia. Computer-Human Interaction, 2006. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/paper673-boyd-graber.pdf Images ------------------------- Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_naacl_colorization.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016. http://umiacs.umd.edu/~jbg/docs/2016_naacl_paintings.pdf Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference of Machine Learning, 2012. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mtibp_icml_2012.pdf Xiaojuan Ma, Jordan Boyd-Graber, Sonya S. Nikolova, and Perry Cook. Speaking Through Pictures: Images vs. Icons. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/image_icon.pdf Interpretability ------------------------- Alison Smith, Jordan Boyd-Graber, Ron Fan, Melissa Birchfield, Tongshuang Wu, Dan Weld, and Leah Findlater. No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML. Computer-Human Interaction, 2020. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_chi_explanation.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Digging into User Control: Perceptions of Adherence and Instability in Transparent Models. Intelligent User Interfaces, 2020. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_iui_control.pdf Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater. Which Evaluations Uncover Sense Representations that Actually Make Sense?. Linguistic Resources and Evaluation Conference, 2020. http://umiacs.umd.edu/~jbg/docs/2020_lrec_sense.pdf Eric Wallace, Shi Feng, and Jordan Boyd-Graber. Misleading Failures of Partial-input Baselines. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_flipside.pdf Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_iui_augment.pdf Shi Feng, Eric Wallace, and Jordan Boyd-Graber. Interpreting Neural Networks with Nearest Neighbors. EMNLP Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2018. http://umiacs.umd.edu/~jbg/http://aclweb.org/anthology/W18-5416 Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Jordan Boyd-Graber. Humans and Computers Working Together to Measure Machine Learning Interpretability. The Bridge, 2017. Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_itm.pdf Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems, 2009. Jonathan Chang and I shared a NIPS student award honorable mention for this paper (5 out of 1105) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Lexical Semantics ------------------------- Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater. Which Evaluations Uncover Sense Representations that Actually Make Sense?. Linguistic Resources and Evaluation Conference, 2020. http://umiacs.umd.edu/~jbg/docs/2020_lrec_sense.pdf Sonya S. Nikolova, Jordan Boyd-Graber, and Christiane Fellbaum. Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools. Modeling, Learning and Processing of Text Technological Data Structures, 2011. http://umiacs.umd.edu/~jbg/docs/2011_book_chapter_evocation.pdf Jordan Boyd-Graber. Linguistic Resource Creation in a Web 2.0 World. NSF Workshop on Collaborative Annotation, 2011. http://umiacs.umd.edu/~jbg/docs/2011_resources.pdf Jordan Boyd-Graber and David M. Blei. PUTOP: Turning Predominant Senses into a Topic Model for WSD. 4th International Workshop on Semantic Evaluations, 2007. http://umiacs.umd.edu/~jbg/docs/jbg-SEMEVAL07.pdf Jordan Boyd-Graber, David M. Blei, and Xiaojin Zhu. A Topic Model for Word Sense Disambiguation. Empirical Methods in Natural Language Processing, 2007. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-EMNLP07.pdf Jordan Boyd-Graber, Christiane Fellbaum, Daniel Osherson, and Robert Schapire. Adding Dense, Weighted, Connections to WordNet. Proceedings of the Global WordNet Conference, 2006. http://umiacs.umd.edu/~jbg/docs/jbg-jeju.pdf MCMC Inference ------------------------- Varun Kumar, Alison Smith, Leah Findlater, Kevin Seppi, and Jordan Boyd-Graber. Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_control.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Multilingual Topic Model for Learning Weighted Topic Links Across Incomparable Corpora. Empirical Methods in Natural Language Processing, 2019. http://umiacs.umd.edu/~jbg/docs/2019_emnlp_mtm.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_tree_prior.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_docblock.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2016. Md Arafat Sultan, Jordan Boyd-Graber, and Tamara Sumner. Bayesian Supervised Domain Adaptation for Short Text Similarity. North American Association for Computational Linguistics, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_sts.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler. Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_teaparty.pdf Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. This paper received the best paper award at CoNLL (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_conll_cslda.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors. Empirical Methods in Natural Language Processing, 2015. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_hinge_link.pdf Yi Yang, Doug Downey, and Jordan Boyd-Graber. Efficient Methods for Incorporating Knowledge into Topic Models. Empirical Methods in Natural Language Processing, 2015. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_fast_priors.pdf Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Viet-An Nguyen, Jordan Boyd-Graber, Jonathan Chang, and Philip Resnik. Tree-Based Label Dependency Topic Models. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Yuening Hu, Jordan Boyd-Graber, Hal Daume III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_coalescent.pdf Yuening Hu and Jordan Boyd-Graber. Efficient Tree-Based Topic Modeling. Association for Computational Linguistics, 2012. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_fttm.pdf Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference of Machine Learning, 2012. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mtibp_icml_2012.pdf Yuening Hu and Jordan Boyd-Graber. Bayesian Hierarchical Clustering with Beta Coalescents. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Machine Translation ------------------------- Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, and Graham Neubig. Automatic Estimation of Simultaneous Interpreter Performance. Association for Computational Linguistics, 2018. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_acl_interpeval.pdf Khanh Nguyen, Jordan Boyd-Graber, and Hal Daume III. Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback. Empirical Methods in Natural Language Processing, 2017. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_bandit_mt.pdf Alvin Grissom II, Naho Orita, and Jordan Boyd-Graber. Incremental Prediction of Sentence-final Verbs. Conference on Computational Natural Language Learning, 2016. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_conll_verbpred.pdf He He, Jordan Boyd-Graber, and Hal Daume III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_interpretese.pdf He He, Alvin Grissom II, Jordan Boyd-Graber, and Hal Daume III. Syntax-based Rewriting for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2015. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_rewrite.pdf Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daume III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_simtrans.pdf Yuening Hu, Ke Zhai, Vlad Edelman, and Jordan Boyd-Graber. Topic Models for Translation Domain Adaptation. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Vladimir Eidelman, Jordan Boyd-Graber, and Philip Resnik. Topic Models for Dynamic Translation Model Adaptation. Association for Computational Linguistics, 2012. For a more thorough evaluation and an exploration of more advanced topic models for machine translation, see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_tm_for_mt.pdf Multilingual Corpora ------------------------- Mozhi Zhang, Yoshinari Fujinuma, Michael J. Paul, and Jordan Boyd-Graber. Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries. Association for Computational Linguistics, 2020. (17.6% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_acl_refine.pdf Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. Association for the Advancement of Artificial Intelligence, 2020. (20.6% Acceptance Rate) http://umiacs.umd.edu/~jbg/https://arxiv.org/abs/1812.09617 Yoshinari Fujinuma, Michael Paul, and Jordan Boyd-Graber. A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity. Association for Computational Linguistics, 2019. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_modularity.pdf Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, and Jordan Boyd-Graber. Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. Association for Computational Linguistics, 2019. (18.3% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_clwe.pdf Dasha Pruss, Yoshinari Fujinuma, Ashlynn Daughton, Michael Paul, Brad Arnot, Danielle Szafir, and Jordan Boyd-Graber. Zika discourse in the Americas: A multilingual topic analysis of Twitter. PlosOne, 2019. http://umiacs.umd.edu/~jbg/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216922 Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. ACL Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing, 2018. Shudong Hao, Michael J. Paul, and Jordan Boyd-Graber. Lessons from the Bible on Modern Topics: Multilingual Topic Model Evaluation on Low-Resource Languages. North American Association for Computational Linguistics, 2018. (35% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_naacl_mltm_eval.pdf Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Jordan Boyd-Graber and Philip Resnik. Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-mlslda-2010.pdf Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models for Unaligned Text. Uncertainty in Artificial Intelligence, 2009. For coverage of current state-of-the-art in cross-lingual topic models see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/uai2009.pdf Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models. NIPS Workshop on Unsupervised Latent Variable Models, 2008. Question Answering ------------------------- Benjamin Börschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, and Lierni Sestorain Saralegu. Meta Answering for Machine Reading. ArXiv, Preprint. http://umiacs.umd.edu/~jbg/docs/2020_acl_refine.pdf Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. http://umiacs.umd.edu/~jbg/https://arxiv.org/abs/1904.04792 Jordan Boyd-Graber and Benjamin Börschinger. What Question Answering can Learn from Trivia Nerds. Association for Computational Linguistics, 2020. (25.4% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_acl_trivia.pdf Chen Zhao, Chenyan Xiong, Xin Qian, and Jordan Boyd-Graber. Complex Factoid Question Answering with a Free-Text Knowledge Graph. The Web Conference, 2020. (19.2% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_www_delft.pdf Eric Wallace, Shi Feng, and Jordan Boyd-Graber. Misleading Failures of Partial-input Baselines. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_flipside.pdf Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, and Jordan Boyd-Graber. Mitigating Noisy Inputs for Question Answering. Conference of the International Speech Communication Association, 2019. http://umiacs.umd.edu/~jbg/docs/2019_interspeech_asr Ahmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. Can You Unpack That? Learning to Rewrite Questions-in-Context. Empirical Methods in Natural Language Processing, 2019. http://umiacs.umd.edu/~jbg/docs/2019_emnlp_sequentialqa.pdf Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_iui_augment.pdf Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Transactions of the Association of Computational Linguistics, 2019. http://umiacs.umd.edu/~jbg/docs/2019_tacl_trick.pdf Eric Wallace and Jordan Boyd-Graber. Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. ACL Student Research Workshop, 2018. http://umiacs.umd.edu/~jbg/http://aclweb.org/anthology/P18-3018 Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. Dataset and Baselines for Sequential Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2018. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_linked.pdf Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_icml_opponent.pdf Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016. http://umiacs.umd.edu/~jbg/docs/2016_naacl_paintings.pdf Md Arafat Sultan, Jordan Boyd-Graber, and Tamara Sumner. Bayesian Supervised Domain Adaptation for Short Text Similarity. North American Association for Computational Linguistics, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_sts.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daume III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015.This won the best demonstration award at NIPS 2015 Anupam Guha, Mohit Iyyer, Danny Bouman, and Jordan Boyd-Graber. Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_qb_coref.pdf Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daume III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_qb_rnn.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf Reinforcement Learning ------------------------- Khanh Nguyen, Jordan Boyd-Graber, and Hal Daume III. Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback. Empirical Methods in Natural Language Processing, 2017. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_bandit_mt.pdf He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_icml_opponent.pdf Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daume III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_simtrans.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf Sentiment and Perspective ------------------------- Denis Peskov, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, and Jordan Boyd-Graber. It Takes Two to Lie: One to Lie and One to Listen. Association for Computational Linguistics, 2020. (25.4% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_acl_diplomacy.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf Vlad Niculae, Srijan Kumar, Jordan Boyd-Graber, and Cristian Danescu-Niculescu-Mizil. Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_diplomacy.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler. Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_teaparty.pdf Stephen H. Bach, Bert Huang, Jordan Boyd-Graber, and Lise Getoor. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning, 2015. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_icml_paired_dual.pdf Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. NAACL Workshop on Cognitive Modeling and Computational Linguistics, 2015. Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_rnn_ideology.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_howto_gibbs.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah Cai, Jennifer Midberry, and Yuanxin Wang. Modeling Topic Control to Detect Influence in Conversations using Nonparametric Topic Models. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_influencer.pdf Kimberly Glasgow, Clay Fink, and Jordan Boyd-Graber. Our grief is unspeakable: Measuring the community impact of a tragedy. The International AAAI Conference on Weblogs and Social Media, 2014. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_icwsm_grief.pdf Jordan Boyd-Graber, Kimberly Glasgow, and Jackie Sauter Zajac. Spoiler Alert: Machine Learning Approaches to Detect Social Media Posts with Revelatory Information. ASIST 2013: The 76th Annual Meeting of the American Society for Information Science and Technology, 2013. http://umiacs.umd.edu/~jbg/docs/2013_spoiler.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_shlda.pdf Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. (50% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_argviz.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations. Association for Computational Linguistics, 2012. (19% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. "I Want to Talk About, Again, My Record On Energy …'': Modeling Topic Control in Conversations using Speaker-centric Nonparametric Topic Models. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Asad B. Sayeed, Jordan Boyd-Graber, Bryan Rusk, and Amy Weinberg. Grammatical structures for word-level sentiment detection. North American Association of Computational Linguistics, 2012. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/srt_naacl_2012.pdf Clay Templeton, Travis Brown, Sayan Battacharyya, and Jordan Boyd-Graber. Mining the Dispatch under Supervision: Using Casualty Counts to Guide Topics from the Richmond Daily Dispatch Corpus. Chicago Colloquium on Digital Humanities and Computer Science, 2011. http://umiacs.umd.edu/~jbg/docs/slda_civil_war.pdf Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment. IEEE International Conference on Social Computing, 2011. (10% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/simulating_audiences.pdf Pranav Anand, Joseph King, Jordan Boyd-Graber, Earl Wagner, Craig Martell, Douglas W. Oard, and Philip Resnik. Believe Me: We Can Do This!. The AAAI 2011 workshop on Computational Models of Natural Argument, 2011. http://umiacs.umd.edu/~jbg/docs/persuasion.pdf Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Comparing Values and Sentiment Using Mechanical Turk. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-comparing.pdf Kenneth R. Fleischmann, Clay Templeton, and Jordan Boyd-Graber. Modeling Diverse Standpoints in Text Classification: Learning to Be Human by Modeling Human Values. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-learning.pdf Jordan Boyd-Graber and Philip Resnik. Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-mlslda-2010.pdf Eric Hardisty, Jordan Boyd-Graber, and Philip Resnik. Modeling Perspective using Adaptor Grammars. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/adapted_naive_bayes.pdf Spectral Methods ------------------------- Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, and Kevin Seppi. Automatic and Human Evaluation of Local Topic Quality. Association for Computational Linguistics, 2019. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_local.pdf Michelle Yuan, Benjamin Van Durme, and Jordan Boyd-Graber. Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. Neural Information Processing Systems, 2018. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_neurips_mtanchor.pdf Jeff Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber. Tandem Anchoring: A Multiword Anchor Approach for Interactive Topic Modeling. Association for Computational Linguistics, 2017. (22% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_acl_multiword_anchors.pdf Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_anchor_reg.pdf Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Evaluating Regularized Anchor Words. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Speech ------------------------- Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, and Jordan Boyd-Graber. Mitigating Noisy Inputs for Question Answering. Conference of the International Speech Communication Association, 2019. http://umiacs.umd.edu/~jbg/docs/2019_interspeech_asr Syntax ------------------------- He He, Jordan Boyd-Graber, and Hal Daume III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_interpretese.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf Anupam Guha, Mohit Iyyer, Danny Bouman, and Jordan Boyd-Graber. Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_qb_coref.pdf Naho Orita, Naomi Feldman, and Jordan Boyd-Graber. Quantifying the role of discourse topicality in speakers' choices of referring expressions. ACL Workshop on Cognitive Modeling and Computational Linguistics, 2014. Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_rnn_ideology.pdf Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daume III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_qb_rnn.pdf Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. Mohit Iyyer, Jordan Boyd-Graber, and Hal Daume III. Generating Sentences from Semantic Vector Space Representations. NIPS Workshop on Learning Semantics, 2014. Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. http://umiacs.umd.edu/~jbg/docs/2014_tacl_ag_vb_online.pdf Naho Orita, Rebecca McKeown, Naomi H. Feldman, Jeffrey Lidz, and Jordan Boyd-Graber. Discovering Pronoun Categories using Discourse Information. Proceedings of the Cognitive Science Society, 2013. http://umiacs.umd.edu/~jbg/docs/2013_cogsci_pronoun.pdf Asad B. Sayeed, Jordan Boyd-Graber, Bryan Rusk, and Amy Weinberg. Grammatical structures for word-level sentiment detection. North American Association of Computational Linguistics, 2012. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/srt_naacl_2012.pdf Jordan Boyd-Graber and David M. Blei. Syntactic Topic Models. Neural Information Processing Systems, 2008. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2008.pdf Topic Models ------------------------- Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Digging into User Control: Perceptions of Adherence and Instability in Transparent Models. Intelligent User Interfaces, 2020. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2020_iui_control.pdf Fenfei Guo, Jordan Boyd-Graber, Mohit Iyyer, and Leah Findlater. Which Evaluations Uncover Sense Representations that Actually Make Sense?. Linguistic Resources and Evaluation Conference, 2020. http://umiacs.umd.edu/~jbg/docs/2020_lrec_sense.pdf Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, and Kevin Seppi. Automatic and Human Evaluation of Local Topic Quality. Association for Computational Linguistics, 2019. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_local.pdf Varun Kumar, Alison Smith, Leah Findlater, Kevin Seppi, and Jordan Boyd-Graber. Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_control.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Multilingual Topic Model for Learning Weighted Topic Links Across Incomparable Corpora. Empirical Methods in Natural Language Processing, 2019. http://umiacs.umd.edu/~jbg/docs/2019_emnlp_mtm.pdf Dasha Pruss, Yoshinari Fujinuma, Ashlynn Daughton, Michael Paul, Brad Arnot, Danielle Szafir, and Jordan Boyd-Graber. Zika discourse in the Americas: A multilingual topic analysis of Twitter. PlosOne, 2019. http://umiacs.umd.edu/~jbg/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216922 Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018.Alison won a best student paper honorable mention (3 out of 300) (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_iui_itm.pdf Michelle Yuan, Benjamin Van Durme, and Jordan Boyd-Graber. Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. Neural Information Processing Systems, 2018. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_neurips_mtanchor.pdf Shudong Hao, Michael J. Paul, and Jordan Boyd-Graber. Lessons from the Bible on Modern Topics: Multilingual Topic Model Evaluation on Low-Resource Languages. North American Association for Computational Linguistics, 2018. (35% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_naacl_mltm_eval.pdf Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei, and James A. Evans. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academies of Science, 2018. Jordan Boyd-Graber, Yuening Hu, and David Mimno. Applications of Topic Models. 2017. http://umiacs.umd.edu/~jbg/http://www.nowpublishers.com/article/Details/INR-030 Jeff Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber. Tandem Anchoring: A Multiword Anchor Approach for Interactive Topic Modeling. Association for Computational Linguistics, 2017. (22% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_acl_multiword_anchors.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. http://umiacs.umd.edu/~jbg/http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_tree_prior.pdf You Lu, Jeff Lund, and Jordan Boyd-Graber. Why ADAGRAD Fails for Online Topic Modeling. Empirical Methods in Natural Language Processing, 2017. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_adagrad_olda.pdf Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_docblock.pdf Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, and Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_doclabel.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2016. Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler. Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_teaparty.pdf Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. This paper received the best paper award at CoNLL (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_conll_cslda.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors. Empirical Methods in Natural Language Processing, 2015. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_hinge_link.pdf Yi Yang, Doug Downey, and Jordan Boyd-Graber. Efficient Methods for Incorporating Knowledge into Topic Models. Empirical Methods in Natural Language Processing, 2015. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_fast_priors.pdf Forough Poursabzi-Sangdeh and Jordan Boyd-Graber. Speeding Document Annotation with Topic Models. NAACL Student Research Workshop, 2015. Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. NAACL Workshop on Cognitive Modeling and Computational Linguistics, 2015. Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Naho Orita, Naomi Feldman, and Jordan Boyd-Graber. Quantifying the role of discourse topicality in speakers' choices of referring expressions. ACL Workshop on Cognitive Modeling and Computational Linguistics, 2014. Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_anchor_reg.pdf Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_howto_gibbs.pdf Jordan Boyd-Graber, David Mimno, and David Newman. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. Handbook of Mixed Membership Models and Their Applications, 2014. http://umiacs.umd.edu/~jbg/docs/2014_book_chapter_care_and_feeding.pdf Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_itm.pdf Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeffrey Heer. Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations. NIPS Workshop on Human-Propelled Machine Learning, 2014. Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Ke Zhai and Jordan Boyd-Graber. Online Topic Models with Infinite Vocabulary. International Conference on Machine Learning, 2013. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_icml_infvoc.pdf Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Evaluating Regularized Anchor Words. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Yuening Hu, Ke Zhai, Vlad Edelman, and Jordan Boyd-Graber. Topic Models for Translation Domain Adaptation. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Viet-An Nguyen, Jordan Boyd-Graber, Jonathan Chang, and Philip Resnik. Tree-Based Label Dependency Topic Models. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_shlda.pdf Naho Orita, Rebecca McKeown, Naomi H. Feldman, Jeffrey Lidz, and Jordan Boyd-Graber. Discovering Pronoun Categories using Discourse Information. Proceedings of the Cognitive Science Society, 2013. http://umiacs.umd.edu/~jbg/docs/2013_cogsci_pronoun.pdf Ke Zhai, Jordan Boyd-Graber, Nima Asadi, and Mohamad Alkhouja. Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce. ACM International Conference on World Wide Web, 2012. (12% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mrlda.pdf Yuening Hu and Jordan Boyd-Graber. Efficient Tree-Based Topic Modeling. Association for Computational Linguistics, 2012. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_fttm.pdf Vladimir Eidelman, Jordan Boyd-Graber, and Philip Resnik. Topic Models for Dynamic Translation Model Adaptation. Association for Computational Linguistics, 2012. For a more thorough evaluation and an exploration of more advanced topic models for machine translation, see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_tm_for_mt.pdf Yuening Hu and Jordan Boyd-Graber. Suggesting Constraints for Interactive Topic Modeling. ICML Workshop on Machine Learning in Human Computation and Crowdsourcing, 2012. Ke Zhai and Jordan Boyd-Graber. Online Topic Model with Infinite Vocabulary. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/itm.pdf Clay Templeton, Travis Brown, Sayan Battacharyya, and Jordan Boyd-Graber. Mining the Dispatch under Supervision: Using Casualty Counts to Guide Topics from the Richmond Daily Dispatch Corpus. Chicago Colloquium on Digital Humanities and Computer Science, 2011. http://umiacs.umd.edu/~jbg/docs/slda_civil_war.pdf Jordan Boyd-Graber. Linguistic Extensions of Topic Models. Ph.D. thesis, Princeton University, 2010. http://umiacs.umd.edu/~jbg/docs/2010_jbg_thesis.pdf Jordan Boyd-Graber and Philip Resnik. Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-mlslda-2010.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Connections between the Lines: Augmenting Social Networks with Text. Knowledge Discovery and Data Mining, 2009. (9% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/kdd2009.pdf Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems, 2009. Jonathan Chang and I shared a NIPS student award honorable mention for this paper (5 out of 1105) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models for Unaligned Text. Uncertainty in Artificial Intelligence, 2009. For coverage of current state-of-the-art in cross-lingual topic models see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/uai2009.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Discovering social networks from free text. 3rd Annual Machine Learning Symposium, 2008. Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models. NIPS Workshop on Unsupervised Latent Variable Models, 2008. Jordan Boyd-Graber and David M. Blei. Syntactic Topic Models. Neural Information Processing Systems, 2008. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2008.pdf Jordan Boyd-Graber and David M. Blei. PUTOP: Turning Predominant Senses into a Topic Model for WSD. 4th International Workshop on Semantic Evaluations, 2007. http://umiacs.umd.edu/~jbg/docs/jbg-SEMEVAL07.pdf Jordan Boyd-Graber, David M. Blei, and Xiaojin Zhu. A Topic Model for Word Sense Disambiguation. Empirical Methods in Natural Language Processing, 2007. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-EMNLP07.pdf Variational Inference ------------------------- Varun Kumar, Alison Smith, Leah Findlater, Kevin Seppi, and Jordan Boyd-Graber. Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models. Association for Computational Linguistics, 2019. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2019_acl_control.pdf Paul Felt, Eric Ringger, Kevin Seppi, and Jordan Boyd-Graber. Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types. International Conference on Computational Linguistics, 2018. (37% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_coling_measurements.pdf Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei, and James A. Evans. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academies of Science, 2018. You Lu, Jeff Lund, and Jordan Boyd-Graber. Why ADAGRAD Fails for Online Topic Modeling. Empirical Methods in Natural Language Processing, 2017. (18% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_emnlp_adagrad_olda.pdf Stephen H. Bach, Bert Huang, Jordan Boyd-Graber, and Lise Getoor. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning, 2015. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_icml_paired_dual.pdf Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. http://umiacs.umd.edu/~jbg/docs/2014_tacl_ag_vb_online.pdf Ke Zhai and Jordan Boyd-Graber. Online Topic Models with Infinite Vocabulary. International Conference on Machine Learning, 2013. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_icml_infvoc.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Connections between the Lines: Augmenting Social Networks with Text. Knowledge Discovery and Data Mining, 2009. (9% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/kdd2009.pdf Jordan Boyd-Graber and David M. Blei. Syntactic Topic Models. Neural Information Processing Systems, 2008. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2008.pdf