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Refining an almost clean translation memory helps machine translation

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Lienhttps://aclanthology.org/2022.amta-research.16/
AuteurRechercher : ; Rechercher : ; Rechercher : ; Rechercher : 1; Rechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches Canada. Technologies numériques
FormatTexte, Article
Conférence15th biennial conference of the Association for Machine Translation in the Americas (AMTM), September 12-16, 2022, Orlando, USA
Résumé
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.
Date de publication
Maison d’éditionAssociation for Machine Translation in the Americas
Licence
Dans
  • Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas. Volume 1. Research Track (septembre 2022) : 215–226.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement387d7827-e302-48ab-a3a9-0efc7a2939bf
Enregistrement créé2022-09-26
Enregistrement modifié2022-09-26

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