Abstract
In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system’s output.- Anthology ID:
- D19-5214
- Volume:
- Proceedings of the 6th Workshop on Asian Translation
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Toshiaki Nakazawa, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Nobushige Doi, Yusuke Oda, Ondřej Bojar, Shantipriya Parida, Isao Goto, Hidaya Mino
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 123–130
- Language:
- URL:
- https://aclanthology.org/D19-5214
- DOI:
- 10.18653/v1/D19-5214
- Cite (ACL):
- Akiko Eriguchi, Spencer Rarrick, and Hitokazu Matsushita. 2019. Combining Translation Memory with Neural Machine Translation. In Proceedings of the 6th Workshop on Asian Translation, pages 123–130, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Combining Translation Memory with Neural Machine Translation (Eriguchi et al., WAT 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D19-5214.pdf