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We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts.
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Recursive deep models for semantic compositionality over a sentiment treebank
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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A neural attention model for abstractive sentence summarization
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, R. Salakhutdinov, R. Zemel, and Yoshua Bengio. 2015 · 2015
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Visualizing and understanding neural models in nlp
J. Li, Xinlei Chen, E. Hovy, and Dan Jurafsky. 2016 · 2016
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“why should I trust you?”: Explaining the predictions of any classifier
Marco Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
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Accountability of ai under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, D. O’Brien, Stuart Schieber, J. Waldo, D. Weinberger, and Alexandra Wood. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, W. Samek, and K. Müller. 2017 · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017 · 2017
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Human-grounded evaluations of explanation methods for text classification
Piyawat Lertvittayakumjorn and Francesca Toni. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R. Thomas McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 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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Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
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Hierarchical interpretations for neural network predictions
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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Understanding convolutional neural networks for text classification
Alon Jacovi, Oren Sar Shalom, and Y. Goldberg. 2018 · 2018
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Constituency parsing with a self-attentive encoder
Nikita Kitaev and D. Klein. 2018 · 2018
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Chandan Singh, W. James Murdoch, and Bin Yu. 2019 · 2019
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Mitigating gender bias in natural language processing: Literature review
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019 · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Protoattend: Attention-based prototypical learning
Sercan Ö. Arik and T. Pfister. 2020 · 2020
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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. 2020 · 2020
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Differentiable reasoning over a virtual knowledge base
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, and William W. Cohen. 2020 · 2020
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal. 2020 · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2020
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Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. 2020 · 2020
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Towards hierarchical importance attribution: Explaining compositional semantics for neural sequence models
Xisen Jin, Zhongyu Wei, Junyi Du, Xiangyang Xue, and Xiang Ren. 2020 · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020 · 2020
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On completeness-aware concept-based explanations in deep neural networks
Chih kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Pradeep Ravikumar, and Tomas Pfister. 2020 · 2020
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Learning to deceive with attention-based explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary C Lipton. 2020 · 2020
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Influence tuning: Demoting spurious correlations via instance attribution and instance-driven updates
Xiaochuang Han and Yulia Tsvetkov. 2021 · 2021
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Combining feature and instance attribution to detect artifacts
Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, and Byron C Wallace. 2021 · 2021
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