Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme 1 , which preserves the original shape and semantic information of the characters, while also being reversible. We show promising results from training Wubi-based models on the characterand subword-level with recurrent as well as convolutional models.
REF ) used the Wubi method to encode Chinese characters.
Character-level Chinese-English Translation through ASCII Encoding
WMT
Computer Science