Evaluating Explanation Methods for Neural Machine Translation
Jierui
Li
author
Lemao
Liu
author
Huayang
Li
author
Guanlin
Li
author
Guoping
Huang
author
Shuming
Shi
author
2020-07
text
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Dan
Jurafsky
editor
Joyce
Chai
editor
Natalie
Schluter
editor
Joel
Tetreault
editor
Association for Computational Linguistics
Online
conference publication
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.
li-etal-2020-evaluating
10.18653/v1/2020.acl-main.35
https://aclanthology.org/2020.acl-main.35
2020-07
365
375