AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Visualizing attention in transformer-based language models

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Roberta: A robustly optimized bert pretraining approach

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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907

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Interpreting neural networks with nearest neighbors

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Eric Wallace, Shi Feng, and Jordan Boyd-Graber. 2018 · 1909

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Deep inside convolutional networks: Visualising image classification models and saliency maps

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Neural machine translation by jointly learning to align and translate

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Visualizing and understanding recurrent networks

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Technical report on the CleverHans v2.1.0 adversarial examples library

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Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, et al. 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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Towards a rigorous science of interpretable machine learning

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Finale Doshi-Velez and Been Kim. 2017 · 2017

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Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning

Andrew P Norton and Yanjun Qi. 2017 · 2017

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Bidirectional attention flow for machine comprehension

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SmoothGrad: removing noise by adding noise

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Axiomatic attribution for deep networks

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017

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HotFlip: White-box adversarial examples for text classification

Annotation artifacts in natural language inference data

Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018

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Visual interrogation of attention-based models for natural language inference and machine comprehension

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Deep contextualized word representations

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018

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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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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs

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