Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation

Kazuma Hashimoto, Akiko Eriguchi, Yoshimasa Tsuruoka


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
Venue:
WAT
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
75–83
Language:
URL:
https://aclanthology.org/W16-4605
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/W16-4605.pdf
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