Character-level Chinese-English Translation through ASCII Encoding
Nikola I. Nikolov, Yuhuang Hu, Mi Xue Tan, Richard H. R. Hahnloser · 2018
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.