Visualizing and Understanding Neural Machine Translation
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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P17-1106.pdf