Intelligent Translation Memory Matching and Retrieval with Sentence Encoders

Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov


Abstract
Matching and retrieving previously translated segments from the Translation Memory is a key functionality in Translation Memories systems. However this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this paper, we introduce sentence encoders to improve matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance-based algorithms.
Anthology ID:
2020.eamt-1.19
Volume:
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
Month:
November
Year:
2020
Address:
Lisboa, Portugal
Editors:
André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
175–184
Language:
URL:
https://aclanthology.org/2020.eamt-1.19
DOI:
Bibkey:
Cite (ACL):
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2020. Intelligent Translation Memory Matching and Retrieval with Sentence Encoders. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 175–184, Lisboa, Portugal. European Association for Machine Translation.
Cite (Informal):
Intelligent Translation Memory Matching and Retrieval with Sentence Encoders (Ranasinghe et al., EAMT 2020)
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PDF:
https://aclanthology.org/2020.eamt-1.19.pdf