  Neural machine translation (NMT) is one of the best methods for understanding
the differences in semantic rules between two languages. Especially for
Indo-European languages, subword-level models have achieved impressive results.
However, when the translation task involves Chinese, semantic granularity
remains at the word and character level, so there is still need more
fine-grained translation model of Chinese. In this paper, we introduce a simple
and effective method for Chinese translation at the sub-character level. Our
approach uses the Wubi method to translate Chinese into English; byte-pair
encoding (BPE) is then applied. Our method for Chinese-English translation
eliminates the need for a complicated word segmentation algorithm during
preprocessing. Furthermore, our method allows for sub-character-level neural
translation based on recurrent neural network (RNN) architecture, without
preprocessing. The empirical results show that for Chinese-English translation
tasks, our sub-character-level model has a comparable BLEU score to the subword
model, despite having a much smaller vocabulary. Additionally, the small
vocabulary is highly advantageous for NMT model compression.
