Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement

Nina Poerner, Hinrich Schütze, Benjamin Roth


Abstract
The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.
Anthology ID:
P18-1032
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
340–350
Language:
URL:
https://www.aclweb.org/anthology/P18-1032
DOI:
10.18653/v1/P18-1032
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-1032.pdf
Note:
 P18-1032.Notes.pdf
Poster:
 P18-1032.Poster.pdf