@inproceedings{nikolov-etal-2018-character,
    title = "Character-level {C}hinese-{E}nglish Translation through {ASCII} Encoding",
    author = "Nikolov, Nikola I.  and
      Hu, Yuhuang  and
      Tan, Mi Xue  and
      Hahnloser, Richard H.R.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-6302",
    doi = "10.18653/v1/W18-6302",
    pages = "10--16",
    abstract = "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, 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 character- and subword-level with recurrent as well as convolutional models.",
}