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Jordan Boyd-Graber
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Reading tea leaves: How humans interpret topic models
J Chang, J Boyd-Graber, S Gerrish, C Wang, DM Blei
Neural Information Processing Systems (NIPS) 31, 2009
34892009
Deep unordered composition rivals syntactic methods for text classification
M Iyyer, V Manjunatha, J Boyd-Graber, H Daumé III
Proceedings of the 53rd annual meeting of the association for computational …, 2015
11472015
Interactive topic modeling
Y Hu, J Boyd-Graber, B Satinoff, A Smith
Machine learning 95, 423-469, 2014
5052014
A neural network for factoid question answering over paragraphs
M Iyyer, J Boyd-Graber, L Claudino, R Socher, H Daumé III
Proceedings of the 2014 conference on empirical methods in natural language …, 2014
4682014
Political ideology detection using recursive neural networks
M Iyyer, P Enns, J Boyd-Graber, P Resnik
Proceedings of the 52nd annual meeting of the Association for Computational …, 2014
4052014
Applications of topic models
J Boyd-Graber, Y Hu, D Mimno
Foundations and Trends® in Information Retrieval 11 (2-3), 143-296, 2017
3992017
Opponent modeling in deep reinforcement learning
H He, J Boyd-Graber, K Kwok, H Daumé III
International conference on machine learning, 1804-1813, 2016
3932016
Pathologies of neural models make interpretations difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
arXiv preprint arXiv:1804.07781, 2018
3732018
A topic model for word sense disambiguation
J Boyd-Graber, D Blei, X Zhu
Conference on Empirical Methods in Natural Language Processing (EMNLP), 1024 …, 2007
3382007
Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter
P Resnik, W Armstrong, L Claudino, T Nguyen, VA Nguyen, ...
Proceedings of the 2nd workshop on computational linguistics and clinical …, 2015
2962015
Syntactic topic models
J Boyd-Graber, DM Blei
Neural Information Processing Systems (NIPS), 2008
2872008
Care and feeding of topic models: Problems, diagnostics, and improvements
J Boyd-Graber, D Mimno, D Newman
Handbook of mixed membership models and their applications 225255, 2014
2852014
Prompting gpt-3 to be reliable
C Si, Z Gan, Z Yang, S Wang, J Wang, J Boyd-Graber, L Wang
arXiv preprint arXiv:2210.09150, 2022
2502022
Can you unpack that? learning to rewrite questions-in-context
A Elgohary, D Peskov, J Boyd-Graber
Can You Unpack That? Learning to Rewrite Questions-in-Context, 2019
2402019
Multilingual topic models for unaligned text
J Boyd-Graber, D Blei
arXiv preprint arXiv:1205.2657, 2012
2232012
Cold-start active learning through self-supervised language modeling
M Yuan, HT Lin, J Boyd-Graber
arXiv preprint arXiv:2010.09535, 2020
2062020
Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce
K Zhai, J Boyd-Graber, N Asadi, ML Alkhouja
Proceedings of the 21st international conference on World Wide Web, 879-888, 2012
2042012
Adding dense, weighted connections to WordNet
J Boyd-Graber, C Fellbaum, D Osherson, R Schapire
Proceedings of the third international WordNet conference, 29-36, 2006
1992006
Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships
M Iyyer, A Guha, S Chaturvedi, J Boyd-Graber, H Daumé III
1832016
Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber
Transactions of the Association for Computational Linguistics 7, 387-401, 2019
1792019
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