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Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions.
The proof and measurement of association between two things
Charles Spearman. 1961 · 1961
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Structural ambiguity and lexical relations
Donald Hindle and Mats Rooth. 1993 · 1993
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Evaluations and methods for explanation through robustness analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, and Cho-Jui Hsieh. 2020 · 2006
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Modeling annotators: A generative approach to learning from annotator rationales
Omar Zaidan and Jason Eisner. 2008 · 2008
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Are interpretations fairly evaluated? a definition driven pipeline for post-hoc interpretability
Ninghao Liu, Yunsong Meng, Xia Hu, Tie Wang, and Bo Long. 2020 · 2009
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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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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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard H. 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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Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D. Manning. 2017 · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 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
Cited alongside, same era.
Achieving verified robustness to symbol substitutions via interval bound propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, and Pushmeet Kohli. 2019 · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019 · 2019
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim. 2019 · 2019
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Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
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An abstract domain for certifying neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev. 2019 · 2019
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David Alvarez-Melis and Tommi S Jaakkola. 2018 · 2018
Cited alongside, same era.
Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross. 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.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli. 2018 · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
On the (in) fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar. 2019 · 2019
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020 · 2020
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The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
Jasmijn Bastings and Katja Filippova. 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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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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Robustness verification for transformers
Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh. 2020 · 2020
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Automatic perturbation analysis for scalable certified robustness and beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh. 2020 · 2020
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Evaluating saliency methods for neural language models
Shuoyang Ding and Philipp Koehn. 2021 · 2021
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