Abstract
This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20% F1 score, achieving state-of-the-art performance on three datasets.- Anthology ID:
- P19-1578
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5780–5785
- Language:
- URL:
- https://aclanthology.org/P19-1578
- DOI:
- 10.18653/v1/P19-1578
- Cite (ACL):
- Dingmin Wang, Yi Tay, and Li Zhong. 2019. Confusionset-guided Pointer Networks for Chinese Spelling Check. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5780–5785, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Confusionset-guided Pointer Networks for Chinese Spelling Check (Wang et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P19-1578.pdf