Abstract
This paper reports our Chinese Grammatical Error Diagnosis system in the NLPTEA-2020 CGED shared task. In 2020, we sent two runs with two approaches. The first one is a combination of conditional random fields (CRF) and a BERT model deep-learning approach. The second one is a BERT model deep-learning approach. The official results shows that our run1 achieved the highest precision rate 0.9875 with the lowest false positive rate 0.0163 on detection, while run2 gives a more balanced performance.- Anthology ID:
- 2020.nlptea-1.12
- Volume:
- Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Erhong YANG, Endong XUN, Baolin ZHANG, Gaoqi RAO
- Venue:
- NLP-TEA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 91–96
- Language:
- URL:
- https://aclanthology.org/2020.nlptea-1.12
- DOI:
- Cite (ACL):
- Shih-Hung Wu and Junwei Wang. 2020. CYUT Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2020 CGED Shared Task. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 91–96, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- CYUT Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2020 CGED Shared Task (Wu & Wang, NLP-TEA 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.nlptea-1.12.pdf