Abstract
With more and more people around the world learning Chinese as a second language, the need of Chinese error correction tools is increasing. In the HSK dynamic composition corpus, word usage error (WUE) is the most common error type. In this paper, we build a neural network model that considers both target erroneous token and context to generate a correction vector and compare it against a candidate vocabulary to propose suitable corrections. To deal with potential alternative corrections, the top five proposed candidates are judged by native Chinese speakers. For more than 91% of the cases, our system can propose at least one acceptable correction within a list of five candidates. To the best of our knowledge, this is the first research addressing general-type Chinese WUE correction. Our system can help non-native Chinese learners revise their sentences by themselves.- Anthology ID:
- C18-1204
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2410–2422
- Language:
- URL:
- https://aclanthology.org/C18-1204
- DOI:
- Cite (ACL):
- Yow-Ting Shiue, Hen-Hsen Huang, and Hsin-Hsi Chen. 2018. Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2410–2422, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Correcting Chinese Word Usage Errors for Learning Chinese as a Second Language (Shiue et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/C18-1204.pdf