Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation
Abstract
This paper describes our UT-KAY system that participated in the Workshop on Asian Translation 2016. Based on an Attention-based Neural Machine Translation (ANMT) model, we build our system by incorporating a domain adaptation method for multiple domains and an attention-based unknown word replacement method. In experiments, we verify that the attention-based unknown word replacement method is effective in improving translation scores in Chinese-to-Japanese machine translation. We further show results of manual analysis on the replaced unknown words.- Anthology ID:
- W16-4605
- Volume:
- Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Toshiaki Nakazawa, Hideya Mino, Chenchen Ding, Isao Goto, Graham Neubig, Sadao Kurohashi, Ir. Hammam Riza, Pushpak Bhattacharyya
- Venue:
- WAT
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 75–83
- Language:
- URL:
- https://aclanthology.org/W16-4605
- DOI:
- Cite (ACL):
- Kazuma Hashimoto, Akiko Eriguchi, and Yoshimasa Tsuruoka. 2016. Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 75–83, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation (Hashimoto et al., WAT 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W16-4605.pdf
- Data
- ASPEC