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Neural rationale models are popular for interpretable predictions of NLP tasks.
Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi S Jaakkola. 2019 · 1910
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WordNet: An Electronic Lexical Database
Christiane Fellbaum. 1998 · 1998
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Nltk: The natural language toolkit
Edward Loper and Steven Bird. 2002 · 2002
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Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2005
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Aligning faithful interpretations with their social attribution
Alon Jacovi and Yoav Goldberg. 2020 · 2006
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Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, and William W. Cohen. 2020 · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 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 Ng, and Christopher Potts. 2013 · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Cited alongside, same era.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Cited alongside, same era.
“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Eraser: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2019 · 2019
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Darpa’s explainable artificial intelligence (xai) program
David Gunning and David Aha. 2019 · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin. 2019 · 2019
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Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018 · 2018
Cited alongside, same era.
Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
Cited alongside, same era.
Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim. 2018 · 2018
Cited alongside, same era.
Do feature attribution methods correctly attribute features?
Yilun Zhou, Serena Booth, Marco Tulio Ribeiro, and Julie Shah. 2022a
Cited in the paper.
Exsum: From local explanations to model understanding
Yilun Zhou, Marco Tulio Ribeiro, and Julie Shah. 2022b · 2019
Later among the works it cites.
Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace. 2020 · 2020
Later among the works it cites.
Bayes-trex: a bayesian sampling approach to model transparency by example
Serena Booth, Yilun Zhou, Ankit Shah, and Julie Shah. 2021 · 2021
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