Attention is not Explanation (original) (raw)

Abstract

Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution over attended-to input units, and this is often presented (at least implicitly) as communicating the relative importance of inputs. However, it is unclear what relationship exists between attention weights and model outputs. In this work we perform extensive experiments across a variety of NLP tasks that aim to assess the degree to which attention weights provide meaningful “explanations” for predictions. We find that they largely do not. For example, learned attention weights are frequently uncorrelated with gradient-based measures of feature importance, and one can identify very different attention distributions that nonetheless yield equivalent predictions. Our findings show that standard attention modules do not provide meaningful explanations and should not be treated as though they do.

Anthology ID:

N19-1357

Volume:

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Month:

June

Year:

2019

Address:

Minneapolis, Minnesota

Editors:

Jill Burstein,Christy Doran,Thamar Solorio

Venue:

NAACL

SIG:

Publisher:

Association for Computational Linguistics

Note:

Pages:

3543–3556

Language:

URL:

https://aclanthology.org/N19-1357/

DOI:

10.18653/v1/N19-1357

Bibkey:

Cite (ACL):

Sarthak Jain and Byron C. Wallace. 2019. Attention is not Explanation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3543–3556, Minneapolis, Minnesota. Association for Computational Linguistics.