Analysis Methods in Neural Language Processing: A Survey

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led r...

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Published inTransactions of the Association for Computational Linguistics Vol. 7; pp. 49 - 72
Main Authors Belinkov, Yonatan, Glass, James
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.01.2019
MIT Press Journals, The
The MIT Press
Subjects
Artificial intelligence
Deep learning
Language
Linguistics
Machine learning
Natural language processing
Neural networks
Trends
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Summary:The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.
Bibliography:04, 2019
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ISSN:2307-387X
2307-387X
DOI:10.1162/tacl_a_00254