Analysis Methods in Neural Language Processing: A Survey

Yonatan Belinkov, James Glass


Abstract
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.
Anthology ID:
Q19-1004
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
Year:
2019
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Brian Roark, Ani Nenkova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
49–72
Language:
URL:
https://aclanthology.org/Q19-1004
DOI:
10.1162/tacl_a_00254
Bibkey:
Cite (ACL):
Yonatan Belinkov and James Glass. 2019. Analysis Methods in Neural Language Processing: A Survey. Transactions of the Association for Computational Linguistics, 7:49–72.
Cite (Informal):
Analysis Methods in Neural Language Processing: A Survey (Belinkov & Glass, TACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/Q19-1004.pdf