Abstract
In this paper, we introduce our system for NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED). In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we treat the grammar error diagnosis (GED) task as a grammatical error correction (GEC) problem and propose a method that incorporates a pre-trained model into an encoder-decoder model to solve this problem.- Anthology ID:
- 2020.nlptea-1.11
- 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:
- 87–90
- Language:
- URL:
- https://aclanthology.org/2020.nlptea-1.11
- DOI:
- Cite (ACL):
- Hongfei Wang and Mamoru Komachi. 2020. TMU-NLP System Using BERT-based Pre-trained Model to the NLP-TEA CGED Shared Task 2020. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 87–90, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- TMU-NLP System Using BERT-based Pre-trained Model to the NLP-TEA CGED Shared Task 2020 (Wang & Komachi, NLP-TEA 2020)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2020.nlptea-1.11.pdf