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Recent research towards understanding neural networks probes models in a top-down manner, but is only able to identify model tendencies that are known a priori.
On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 1903
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907
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A weighted kendall’s tau statistic
Grace S Shieh. 1998 · 1998
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Nltk: the natural language toolkit
Edward Loper and Steven Bird. 2002 · 2002
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Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining
Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010 · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013 · 2013
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Glove: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan L. Boyd-Graber, and Hal Daumé III. 2015 · 2015
Cited alongside, same era.
Do supervised distributional methods really learn lexical inference relations?
Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. 2015 · 2015
Cited alongside, same era.
Rationalizing Neural Predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Cited alongside, same era.
Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard H. Hovy, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
W James Murdoch, Peter J Liu, and Bin Yu. 2018 · 2018
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What do Deep Networks Like to See?
Sebastian Palacio, Joachim Folz, Jörn Hees, Federico Raue, Damian Borth, and Andreas Dengel. 2018 · 2018
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Dissecting Contextual Word Embeddings: Architecture and Representation
Matthew E Peters, Mark Neumann, Luke Zettlemoyer, Wen-tau Yih, Paul G Allen, and Computer Science. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Anchors: High-Precision Model-Agnostic Explanations
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
Cited alongside, same era.
Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts
Franck Dernoncourt and Ji Young Lee. 2017 · 2017
Cited alongside, same era.
Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
Cited alongside, same era.
Deriving Machine Attention from Human Rationales
Yujia Bao, Shiyu Chang, Mo Yu, Regina Barzilay, and Computer Science. 2018 · 2018
Cited alongside, same era.
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, and Wei Wang. 2018 · 2018
Cited alongside, same era.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya. 2018 · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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Exploring Semantic Properties of Sentence Embeddings
Xunjie Zhu, Tingfeng Li, and Gerard De Melo. 2018 · 2018
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Interpretable neural predictions with differentiable binary variables
Joost Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
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Identifying and Controlling Important Neurons in Neural Machine Translation
Anthony Bau, Nadir Durrani, Yonatan Belinkov, Fahim Dalvi, Hassan Sajjad, and James Glass. 2019 · 2019
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Analysis Methods in Neural Language Processing: A Survey
Yonatan Belinkov and James Glass. 2019 · 2019
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