  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.
