System Report for CCL23-Eval Task 8: Chinese Grammar Error Detection and Correction Using Multi-Granularity Information

Yixuan Wang, Yijun Liu, Bo Sun, Wanxiang Che

Abstract
“This paper introduces our system at CCL-2023 Task: Chinese Essay Fluency Evaluation (CEFE).The CEFE task aims to study the identification and correction of grammatical errors in primaryand middle school students’ test compositions. The evaluation has three tracks to examine therecognition of wrong sentence types, character-level error correction, and wrong sentence rewrit-ing. According to the task characteristics and data distribution of each track, we propose a token-level discriminative model based on sequence labeling for the multi-label classification task ofwrong sentences, an auto-encoder model based on edited labels for character-level error correc-tion and a seq2seq model obtained by pre-training on pseudo data and fine-tuning on labeleddata to solve the wrong sentence rewriting task. In the final evaluation results, the method weproposed won the first place in all three tracks according to the corresponding evaluation metrics.”
Anthology ID:
2023.ccl-3.30
Volume:
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
Month:
August
Year:
2023
Address:
Harbin, China
Editors:
Maosong Sun, Bing Qin, Xipeng Qiu, Jing Jiang, Xianpei Han
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
271–281
Language:
English
URL:
https://aclanthology.org/2023.ccl-3.30
DOI:
Bibkey:
Cite (ACL):
Yixuan Wang, Yijun Liu, Bo Sun, and Wanxiang Che. 2023. System Report for CCL23-Eval Task 8: Chinese Grammar Error Detection and Correction Using Multi-Granularity Information. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 271–281, Harbin, China. Chinese Information Processing Society of China.
Cite (Informal):
System Report for CCL23-Eval Task 8: Chinese Grammar Error Detection and Correction Using Multi-Granularity Information (Wang et al., CCL 2023)
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