Combining Translation Memory with Neural Machine Translation

Akiko Eriguchi, Spencer Rarrick, Hitokazu Matsushita


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D19-5214.pdf