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MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

2022-04-23 05:20:38
Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang
arXiv_CL
     
Abstract

This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three different Chinese-as-a-Second-Language (CSL) learner sources. Each sentence has been corrected by three annotators, and their corrections are meticulously reviewed by an expert, resulting in 2.3 references per sentence. We conduct experiments with two mainstream CGEC models, i.e., the sequence-to-sequence (Seq2Seq) model and the sequence-to-edit (Seq2Edit) model, both enhanced with large pretrained language models (PLMs), achieving competitive benchmark performance on previous and our datasets. We also discuss CGEC evaluation methodologies, including the effect of multiple references and using a char-based metric. Our annotation guidelines, data, and code are available at \url{this https URL}.

Abstract (translated)

URL

https://arxiv.org/abs/2204.10994

PDF

https://arxiv.org/pdf/2204.10994.pdf

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