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Explainability is a topic of growing importance in NLP.
Convention: A philosophical study
David K. Lewis. 1969 · 1969
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Possible generalization of boltzmann-gibbs statistics
Constantino Tsallis. 1988 · 1988
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Word association norms, mutual information, and lexicography
Kenneth Ward Church and Patrick Hanks. 1989 · 1989
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Regression shrinkage and selection via the lasso
Robert Tibshirani. 1996 · 1996
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Wrappers for feature subset selection
Ron Kohavi and George H. John. 1997 · 1997
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Gene selection for cancer classification using support vector machines
Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik. 2002 · 2002
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An introduction to variable and feature selection
Isabelle Guyon and André Elisseeff. 2003 · 2003
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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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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Sparse overcomplete word vector representations
Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, Chris Dyer, and Noah A. Smith. 2015 · 2015
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Explaining predictions of non-linear classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson. 2016 · 2016
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Multi-agent cooperation and the emergence of (natural) language
Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni. 2016 · 2016
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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
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016a · 2016
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From softmax to sparsemax: A sparse model of attention and multi-label classification
Andre Martins and Ramon Astudillo. 2016 · 2016
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A decomposable attention model for natural language inference
Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 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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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie. 2005 · 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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Explaining recurrent neural network predictions in sentiment analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
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Overview of the iwslt 2017 evaluation campaign
Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Niehues Jan, Stüker Sebastian, Sudoh Katsuitho, Yoshino Koichiro, and Federmann Christian. 2017 · 2017
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Towards a rigorous science of interpretable machine learning
Spine: Sparse interpretable neural embeddings
Anant Subramanian, Danish Pruthi, Harsh Jhamtani, Taylor Berg-Kirkpatrick, and Eduard Hovy. 2018 · 2018
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Learning and evaluating sparse interpretable sentence embeddings
Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko, and Thomas Hofmann. 2018 · 2018
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Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
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Adaptively sparse transformers
Gonçalo M. Correia, Vlad Niculae, and André F. T. Martins. 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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Saliency-driven word alignment interpretation for neural machine translation
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Been Doshi-Velez, Finale; Kim. 2017 · 2017
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Neural Network Methods in Natural Language Processing
Yoav Goldberg and Graeme Hirst. 2017 · 2017
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Emergence of language with multi-agent games: Learning to communicate with sequences of symbols
Serhii Havrylov and Ivan Titov. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross. 2018 · 2018
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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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Shuoyang Ding, Hainan Xu, and Philipp Koehn. 2019 · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Joey NMT: A minimalist NMT toolkit for novices
Julia Kreutzer, Jasmijn Bastings, and Stefan Riezler. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Sparse sequence-to-sequence models
Ben Peters, Vlad Niculae, and André F. T. Martins. 2019 · 2019
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Human-centered artificial intelligence and machine learning
Mark O Riedl. 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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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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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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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2020
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015 · 2057
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