Visualizing and Understanding Neural Machine Translation

Yanzhuo Ding, Yang Liu, Huanbo Luan, Maosong Sun

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Abstract
While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.
Anthology ID:
P17-1106
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1150–1159
Language:
URL:
https://aclanthology.org/P17-1106
DOI:
10.18653/v1/P17-1106
Bibkey:
Cite (ACL):
Yanzhuo Ding, Yang Liu, Huanbo Luan, and Maosong Sun. 2017. Visualizing and Understanding Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1150–1159, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Visualizing and Understanding Neural Machine Translation (Ding et al., ACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/P17-1106.pdf
Video:
 https://preview.aclanthology.org/teach-a-man-to-fish/P17-1106.mp4