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Attribution methods assess the contribution of inputs to the model prediction.
Attention interpretability across NLP tasks
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Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
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Recursive deep models for semantic compositionality over a sentiment treebank
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
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Stochastic backpropagation and approximate inference in deep generative models
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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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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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Interactive and interpretable machine learning models for human machine collaboration
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Understanding neural networks through representation erasure
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SQuAD: 100,000+ questions for machine comprehension of text
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“why should I trust you?”: Explaining the predictions of any classifier
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Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, and Phil Blunsom. 2016 · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
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Neural network methods for natural language processing
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The concrete distribution: A continuous relaxation of discrete random variables
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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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Towards a deep and unified understanding of deep neural models in NLP
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Visualizing and understanding the effectiveness of BERT
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Improving fairness in machine learning systems: What do industry practitioners need?
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Attention is not Explanation
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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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Diego Marcheggiani and Ivan Titov. 2017 · 2017
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Automatic rule extraction from long short term memory networks
W James Murdoch and Arthur Szlam. 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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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling. 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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Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. 2018 · 2018
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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig. 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
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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Infomask: Masked variational latent representation to localize chest disease
Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, and Yoshua Bengio. 2019 · 2019
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What do you learn from context? probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Sam Bowman, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
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The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives
Elena Voita, Rico Sennrich, and Ivan Titov. 2019a · 2019
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GNNExplainer: Generating Explanations for Graph Neural Networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
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Lookahead optimizer: k steps forward, 1 step back
Michael Zhang, James Lucas, Jimmy Ba, and Geoffrey E Hinton. 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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Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Xisen Jin, Junyi Du, Zhongyu Wei, Xiangyang Xue, and Xiang Ren. 2020 · 2020
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf. 2020 · 2020
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When Explanations Lie: Why Many Modified BP Attributions Fail
Leon Sixt, Maximilian Granz, and Tim Landgraf. 2020 · 2020
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Visualizing the impact of feature attribution baselines
Pascal Sturmfels, Scott Lundberg, and Su-In Lee. 2020 · 2020
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