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Communication Dans Un Congrès Année : 2021

Optimizing Word Alignments with Better Subword Tokenization

Anh Khoa Ngo Ho
François Yvon

Résumé

Word alignments identify translational correspondences between words in a parallel sentence pair and are used, for example, to train statistical machine translation, learn bilingual dictionaries or to perform quality estimation. Subword tokenization has become a standard preprocessing step for a large number of applications, notably for state-of-the-art open vocabulary machine translation systems. In this paper, we thoroughly study how this preprocessing step interacts with the word alignment task and propose several tokenization strategies to obtain well-segmented parallel corpora. Using these new techniques, we were able to improve baseline word-based alignment models for six language pairs.
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Dates et versions

hal-03322842 , version 1 (19-08-2021)

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  • HAL Id : hal-03322842 , version 1

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Anh Khoa Ngo Ho, François Yvon. Optimizing Word Alignments with Better Subword Tokenization. The 18th biennial conference of the International Association of Machine Translation, Aug 2021, Miami (virtual), United States. ⟨hal-03322842⟩
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