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Interpretability or explainability is an emerging research field in NLP.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams. 1992 · 1992
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A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2002
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
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Introduction to Information Retrieval
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2009 · 2009
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Quantitative legal prediction-or-how I learned to stop worrying and start preparing for the data-driven future of the legal services industry
Daniel Martin Katz. 2012 · 2012
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jim Ba. 2015 · 2015
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Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective
Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, and Vasileios Lampos. 2016 · 2016
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016 · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Hierarchical attention networks for document classification
Zichao Yang et al. 2016 · 2016
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Rationale-augmented convolutional neural networks for text classification
Ye Zhang, Iain Marshall, and Byron C. Wallace. 2016 · 2016
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi Jaakkola. 2017 · 2017
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European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman. 2017 · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. 2017 · 2017
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Multiplicative normalizing flows for variational bayesian neural networks
Christos Louizos and Max Welling. 2017 · 2017
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Learning to predict charges for criminal cases with legal basis
Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid. 2018 · 2018
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Harris, O’Boyle, and Warbrick : Law of the European Convention on Human Rights , 4rd edition. edition
David John Harris. 2018 · 2018
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Few-shot charge prediction with discriminative legal attributes
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 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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Attention is not explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Revealing the dark secrets of BERT
Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky. 2019 · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
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Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
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Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Looking beyond the surface:a challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
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The mythos of model interpretability
Zachary C. Lipton. 2018 · 2018
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A test collection for evaluating legal case law search
Daniel Locke and Guido Zuccon. 2018 · 2018
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Judicial decisions of the European Court of Human Rights: Looking into the crystal ball
Masha Medvedeva, Michel Vols, and Martijn Wieling. 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
Cited alongside, same era.
Legal judgment prediction via topological learning
Haoxi Zhong, Guo Zhipeng, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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The Cambridge Handbook of the Law of Algorithms
Woodrow Barfield. 2020 · 2020
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Legal-bert: The muppets straight out of law school
Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 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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Reducing sentiment bias in language models via counterfactual evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. 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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Beyond 512 tokens: Siamese multi-depth transformer-based hierarchical encoder for long-form document matching
Liu Yang, Mingyang Zhang, Cheng Li, Michael Bendersky, and Marc Najork. 2020 · 2020
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How does NLP benefit legal system: A summary of legal artificial intelligence
Haoxi Zhong, Chaojun Xiao, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. 2020 · 2020
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Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT 2021) . Association for Computing Machinery, Online
Madeleine Clare Elish, William Isaac, and Richard Zemel, editors. 2021 · 2021
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