Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation

Seiichiro Kondo, Kengo Hotate, Tosho Hirasawa, Masahiro Kaneko, Mamoru Komachi


Abstract
Recently, neural machine translation is widely used for its high translation accuracy, but it is also known to show poor performance at long sentence translation. Besides, this tendency appears prominently for low resource languages. We assume that these problems are caused by long sentences being few in the train data. Therefore, we propose a data augmentation method for handling long sentences. Our method is simple; we only use given parallel corpora as train data and generate long sentences by concatenating two sentences. Based on our experiments, we confirm improvements in long sentence translation by proposed data augmentation despite the simplicity. Moreover, the proposed method improves translation quality more when combined with back-translation.
Anthology ID:
2021.naacl-srw.18
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
143–149
Language:
URL:
https://aclanthology.org/2021.naacl-srw.18
DOI:
10.18653/v1/2021.naacl-srw.18
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-srw.18.pdf