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One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller. 2010 · 2010
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Learning word vectors for sentiment analysis
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Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Deep unordered composition rivals syntactic methods for text classification
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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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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Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
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Reading wikipedia to answer open-domain questions
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Vahid Kazemi and Ali Elqursh. 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
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. 2017 · 2017
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Bilateral multi-perspective matching for natural language sentences
Zhiguo Wang, Wael Hamza, and Radu Florian. 2017 · 2017
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2018 · 2018
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Annotation artifacts in natural language inference data
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Amirata Ghorbani, Abubakar Abid, and James Y. Zou. 2017 · 2017
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
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Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard H. Hovy, and Daniel Jurafsky. 2016a
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Daniel Jurafsky. 2016b
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Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
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Semantically equivalent adversarial rules for debugging nlp models
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