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There is a recent surge of interest in using attention as explanation of model predictions, with mixed evidence on whether attention can be used as such.
Explaining explanations: Axiomatic feature interactions for deep networks
Joseph D Janizek, Pascal Sturmfels, and Su-In Lee. 2020 · 2002
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Interpreting individual classifications of hierarchical networks
W. Landecker, M. D. Thomure, L. M. A. Bettencourt, M. Mitchell, G. T. Kenyon, and S. P. Brumby. 2013 · 2013
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus. 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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Extraction of salient sentences from labelled documents
Misha Denil, Alban Demiraj, and Nando de Freitas. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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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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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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Explaining recurrent neural network predictions in sentiment analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
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Representation of linguistic form and function in recurrent neural networks
Ákos Kádár, Grzegorz Chrupała, and Afra Alishahi. 2017 · 2017
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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. 2017 · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Latent alignment and variational attention
Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, and Alexander Rush. 2018 · 2018
Cited alongside, same era.
The mythos of model interpretability
Zachary C. Lipton. 2018 · 2018
Cited alongside, same era.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Nina Poerner, Hinrich Schütze, and Benjamin Roth. 2018 · 2018
Layer-wise relevance propagation: An overview
Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller. 2019 · 2019
Later among the works it cites.
Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
Later among the works it cites.
Do human rationales improve machine explanations?
Julia Strout, Ye Zhang, and Raymond Mooney. 2019 · 2019
Later among the works it cites.
Attention interpretability across nlp tasks
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui. 2019 · 2019
Later among the works it cites.
A multiscale visualization of attention in the transformer model
Jesse Vig. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
Linguistically-informed self-attention for semantic role labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
Gradient-Based Attribution Methods , pages 169–191. Springer International Publishing, Cham
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross. 2019 · 2019
Cited alongside, same era.
Evaluating recurrent neural network explanations
Leila Arras, Ahmed Osman, Klaus-Robert Müller, and Wojciech Samek. 2019 · 2019
Cited alongside, same era.
Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019 · 2019
Cited alongside, same era.
Saliency-driven word alignment interpretation for neural machine translation
Shuoyang Ding, Hainan Xu, and Philipp Koehn. 2019 · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim. 2019 · 2019
Cited alongside, same era.
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 2019
Later among the works it cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Later among the works it cites.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema. 2020 · 2020
Closest in time.
How do decisions emerge across layers in neural models? interpretation with differentiable masking
Nicola DeCao, Michael Schlichtkrull, Wilker Aziz, and Ivan Titov. 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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Why attention is not explanation: Surgical intervention and causal reasoning about neural models
Christopher Grimsley, Elijah Mayfield, and Julia R.S. Bursten. 2020 · 2020
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exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
Benjamin Hoover, Hendrik Strobelt, and Sebastian Gehrmann. 2020 · 2020
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Towards transparent and explainable attention models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran. 2020 · 2020
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Learning to deceive with attention-based explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary C. Lipton. 2020 · 2020
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Staying true to your word: (how) can attention become explanation?
Martin Tutek and Jan Snajder. 2020 · 2020
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