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Refining an almost clean translation memory helps machine translation

From National Research Council Canada

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Linkhttps://aclanthology.org/2022.amta-research.16/
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Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Article
Conference15th biennial conference of the Association for Machine Translation in the Americas (AMTM), September 12-16, 2022, Orlando, USA
Abstract
While recent studies have been dedicated to cleaning very noisy parallel corpora to improve Machine Translation training, we focus in this work on filtering a large and mostly clean Translation Memory. This problem of practical interest has not received much consideration from the community, in contrast with, for example, filtering large web-mined parallel corpora. We experiment with an extensive, multi-domain proprietary Translation Memory and compare five approaches involving deep-, feature-, and heuristic-based solutions. We propose two ways of evaluating this task, manual annotation and resulting Machine Translation quality. We report significant gains over a state-of-the-art, off-the-shelf cleaning system, using two MT engines.
Publication date
PublisherAssociation for Machine Translation in the Americas
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  • Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas. Volume 1. Research Track (September 2022): 215–226.
LanguageEnglish
Peer reviewedYes
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Record identifier387d7827-e302-48ab-a3a9-0efc7a2939bf
Record created2022-09-26
Record modified2022-09-26
Date modified: