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Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information

2019-03-01 03:57:15
Longtu Zhang, Mamoru Komachi
arXiv_CL
       
Abstract

Unsupervised neural machine translation (UNMT) requires only monolingual data of similar language pairs during training and can produce bi-directional translation models with relatively good performance on alphabetic languages (Lample et al., 2018). However, no research has been done to logographic language pairs. This study focuses on Chinese-Japanese UNMT trained by data containing sub-character (ideograph or stroke) level information which is decomposed from character level data. BLEU scores of both character and sub-character level systems were compared against each other and the results showed that despite the effectiveness of UNMT on character level data, sub-character level data could further enhance the performance, in which the stroke level system outperformed the ideograph level system.

Abstract (translated)

URL

https://arxiv.org/abs/1903.00149

PDF

https://arxiv.org/pdf/1903.00149.pdf

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