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Neural machine translation (NMT) is one of the best methods for understandingthe differences in semantic rules between two languages. Especially forIndo-European languages, subword-level models have achieved impressive results.However, when the translation task involves Chinese, semantic granularityremains at the word and character level, so there is still need morefine-grained translation model of Chinese. In this paper, we introduce a simpleand effective method for Chinese translation at the sub-character level. Ourapproach uses the Wubi method to translate Chinese into English; byte-pairencoding (BPE) is then applied. Our method for Chinese-English translationeliminates the need for a complicated word segmentation algorithm duringpreprocessing. Furthermore, our method allows for sub-character-level neuraltranslation based on recurrent neural network (RNN) architecture, withoutpreprocessing. The empirical results show that for Chinese-English translationtasks, our sub-character-level model has a comparable BLEU score to the subwordmodel, despite having a much smaller vocabulary. Additionally, the smallvocabulary is highly advantageous for NMT model compression.

SubCharacter Chinese-English Neural Machine Translation with Wubi encoding