Abstract
This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT’16) and their results in the English-to-Japanese translation task. Our model is based on the tree-to-sequence Attention-based NMT (ANMT) model proposed by Eriguchi et al. (2016). We submitted two ANMT systems: one with a word-based decoder and the other with a character-based decoder. Experimenting on the English-to-Japanese translation task, we have confirmed that the character-based decoder can cover almost the full vocabulary in the target language and generate translations much faster than the word-based model.- Anthology ID:
- W16-4617
- 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:
- 175–183
- Language:
- URL:
- https://aclanthology.org/W16-4617
- DOI:
- Cite (ACL):
- Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2016. Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 175–183, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation (Eriguchi et al., WAT 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W16-4617.pdf
- Data
- ASPEC