Translation Memory Retrieval Using Lucene

Kwang-hyok Kim, Myong-ho Cho, Chol-ho Ryang, Ju-song Im, Song-yong Cho, Yong-jun Han


Abstract
Translation Memory (TM) system, a major component of computer-assisted translation (CAT), is widely used to improve human translators’ productivity by making effective use of previously translated resource. We propose a method to achieve high-speed retrieval from a large translation memory by means of similarity evaluation based on vector model, and present the experimental result. Through our experiment using Lucene, an open source information retrieval search engine, we conclude that it is possible to achieve real-time retrieval speed of about tens of microseconds even for a large translation memory with 5 million segment pairs.
Anthology ID:
2021.ranlp-1.78
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
684–691
Language:
URL:
https://aclanthology.org/2021.ranlp-1.78
DOI:
Bibkey:
Cite (ACL):
Kwang-hyok Kim, Myong-ho Cho, Chol-ho Ryang, Ju-song Im, Song-yong Cho, and Yong-jun Han. 2021. Translation Memory Retrieval Using Lucene. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 684–691, Held Online. INCOMA Ltd..
Cite (Informal):
Translation Memory Retrieval Using Lucene (Kim et al., RANLP 2021)
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PDF:
https://aclanthology.org/2021.ranlp-1.78.pdf