Character-level Chinese-English Translation through ASCII Encoding
Nikola I. Nikolov, Yuhuang Hu, Mi Xue Tan, Richard H.R. Hahnloser
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.- Anthology ID:
- W18-6302
- Volume:
- Proceedings of the Third Conference on Machine Translation: Research Papers
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10–16
- Language:
- URL:
- https://aclanthology.org/W18-6302
- DOI:
- 10.18653/v1/W18-6302
- Cite (ACL):
- Nikola I. Nikolov, Yuhuang Hu, Mi Xue Tan, and Richard H.R. Hahnloser. 2018. Character-level Chinese-English Translation through ASCII Encoding. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 10–16, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Character-level Chinese-English Translation through ASCII Encoding (Nikolov et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6302.pdf
- Code
- duguyue100/wmt-en2wubi