Evaluating Explanation Methods for Neural Machine Translation

Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, Shuming Shi


Abstract
Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words that are not aligned to any source word. This paper thereby makes an initial attempt to evaluate explanation methods from an alternative viewpoint. To this end, it proposes a principled metric based on fidelity in regard to the predictive behavior of the NMT model. As the exact computation for this metric is intractable, we employ an efficient approach as its approximation. On six standard translation tasks, we quantitatively evaluate several explanation methods in terms of the proposed metric and we reveal some valuable findings for these explanation methods in our experiments.
Anthology ID:
2020.acl-main.35
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
365–375
Language:
URL:
https://aclanthology.org/2020.acl-main.35
DOI:
10.18653/v1/2020.acl-main.35
Bibkey:
Cite (ACL):
Jierui Li, Lemao Liu, Huayang Li, Guanlin Li, Guoping Huang, and Shuming Shi. 2020. Evaluating Explanation Methods for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 365–375, Online. Association for Computational Linguistics.
Cite (Informal):
Evaluating Explanation Methods for Neural Machine Translation (Li et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.35.pdf
Video:
 http://slideslive.com/38929375