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Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop

Published online by Cambridge University Press:  31 July 2019

Afra Alishahi ,
Grzegorz Chrupała  and
Tal Linzen
Afra Alishahi*
Affiliation:
Department of Cognitive Science and Artificial Intelligence, Tilburg University, The Netherlands
Grzegorz Chrupała
Affiliation:
Department of Cognitive Science and Artificial Intelligence, Tilburg University, The Netherlands
Tal Linzen
Affiliation:
Department of Cognitive Science, Johns Hopkins UniversityBaltimore, United States
*
*Corresponding author. Email: A.Alishahi@uvt.nl
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Abstract

The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.

Keywords

neural networksinterpretabilitynatural language processing
Type
Article
Information
Natural Language Engineering , Volume 25 , Issue 4 , July 2019 , pp. 543 - 557
Copyright
© Cambridge University Press 2019 

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