2022
COLING
COLING 2022
Does partial pretranslation can improve low ressourced-languages pairs?
Abstract
AbstractWe study the effects of a local and punctual pretranslation of the source corpus on the performance of a Transformer translation model. The pretranslations are performed at the morphological (morpheme translation), lexical (word translation) and morphosyntactic (numeral groups and dates) levels. We focus on small and medium-sized training corpora (50K 2.5M bisegments) and on a linguistically distant language pair (Japanese and French). We find that this type of pretranslation does not lead to significant progress. We describe the motivations of the approach, the specific difficulties of Japanese-French translation. We discuss the possible reasons for the observed underperformance.
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The Questioner
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Keyword Pioneer
— japanese-french translation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio