Abstract
In East Asian languages such as Japanese and Chinese, the semantics of a character are (somewhat) reflected in its sub-character elements. This paper examines the effect of using sub-characters for language modeling in Japanese. This is achieved by decomposing characters according to a range of character decomposition datasets, and training a neural language model over variously decomposed character representations. Our results indicate that language modelling can be improved through the inclusion of sub-characters, though this result depends on a good choice of decomposition dataset and the appropriate granularity of decomposition.- Anthology ID:
- W17-4122
- Volume:
- Proceedings of the First Workshop on Subword and Character Level Models in NLP
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Manaal Faruqui, Hinrich Schuetze, Isabel Trancoso, Yadollah Yaghoobzadeh
- Venue:
- SCLeM
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 148–153
- Language:
- URL:
- https://aclanthology.org/W17-4122
- DOI:
- 10.18653/v1/W17-4122
- Cite (ACL):
- Viet Nguyen, Julian Brooke, and Timothy Baldwin. 2017. Sub-character Neural Language Modelling in Japanese. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 148–153, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Sub-character Neural Language Modelling in Japanese (Nguyen et al., SCLeM 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W17-4122.pdf