Bilingual Subword Segmentation for Neural Machine Translation

Hiroyuki Deguchi, Masao Utiyama, Akihiro Tamura, Takashi Ninomiya, Eiichiro Sumita


Abstract
This paper proposed a new subword segmentation method for neural machine translation, “Bilingual Subword Segmentation,” which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation. While existing subword segmentation methods tokenize a sentence without considering its translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences; this method could be more favorable to machine translation. Evaluations on WAT Asian Scientific Paper Excerpt Corpus (ASPEC) English-to-Japanese and Japanese-to-English translation tasks and WMT14 English-to-German and German-to-English translation tasks show that our bilingual subword segmentation improves the performance of Transformer neural machine translation (up to +0.81 BLEU).
Anthology ID:
2020.coling-main.378
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4287–4297
Language:
URL:
https://aclanthology.org/2020.coling-main.378
DOI:
10.18653/v1/2020.coling-main.378
Bibkey:
Cite (ACL):
Hiroyuki Deguchi, Masao Utiyama, Akihiro Tamura, Takashi Ninomiya, and Eiichiro Sumita. 2020. Bilingual Subword Segmentation for Neural Machine Translation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4287–4297, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Bilingual Subword Segmentation for Neural Machine Translation (Deguchi et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.coling-main.378.pdf