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Modern deep learning models for NLP are notoriously opaque.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1906
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HuggingFace’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Case-based explanation of non-case-based learning methods
Rich Caruana, Hooshang Kangarloo, John David N. Dionisio, Usha S. Sinha, and David B. Johnson. 1999 · 1999
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2004
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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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi S. Jaakkola. 2016 · 2016
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“why should i trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Second-order stochastic optimization for machine learning in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan. 2017 · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 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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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 B. Viégas, and Martin Wattenberg. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 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
Cited alongside, same era.
HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
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Pathologies of neural models make interpretation difficult
Shi Feng, Eric Wallace, Alvin Grissom, Mohit Iyyer, Pedro Rodriguez, and Jordan L. Boyd-Graber. 2018 · 2018
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Annotation artifacts in natural language inference data
Auditing deep learning processes through kernel-based explanatory models
Danilo Croce, Daniele Rossini, and Roberto Basili. 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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Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
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Attention is not explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Topics to avoid: Demoting latent confounds in text classification
Sachin Kumar, Shuly Wintner, Noah A. Smith, and Yulia Tsvetkov. 2019 · 2019
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Discovering and controlling for latent confounds in text classification using adversarial domain adaptation
Virgile Landeiro, Tuan Tran, and Aron Culotta. 2019 · 2019
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Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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The mythos of model interpretability
Zachary Chase Lipton. 2018 · 2018
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Deconfounded lexicon induction for interpretable social science
Reid Pryzant, Kelly Shen, Dan Jurafsky, and Stefan Wagner. 2018 · 2018
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Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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Analysis methods in neural language processing: A survey
Yonatan Belinkov and James Glass. 2019 · 2019
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Deep weighted averaging classifiers
Dallas Card, Michael Zhang, and Noah A. Smith. 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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AllenNLP interpret: A framework for explaining predictions of NLP models
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, and Sameer Singh. 2019 · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
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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, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. 2015 · 2019
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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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