论文标题
MUCGEC：用于中国语法误差校正的多参考多源评估数据集
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
论文作者
论文摘要
本文介绍了MUCGEC，这是一种用于中国语法误差校正（CGEC）（CGEC）的多源多源评估数据集，由7,063个句子组成，这些句子从三个中文AS-A-AS-A-Second语言（CSL）学习者来源中收集。每个句子都由三个注释者纠正，并由高级注释者仔细审查其更正，每句话有2.3个参考文献。我们使用两个主流CGEC模型进行实验，即序列到序列模型和序列到编辑模型，均通过较大的预审前的语言模型增强，从而在上一个和我们的数据集上实现了竞争性基准性能。我们还讨论了CGEC评估方法，包括多个参考的效果和使用基于char的指标。我们的注释指南，数据和代码可在\ url {https://github.com/hillzhang1999/mucgec}上获得。
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 Chinese-as-a-Second-Language (CSL) learner sources. Each sentence is corrected by three annotators, and their corrections are carefully reviewed by a senior annotator, resulting in 2.3 references per sentence. We conduct experiments with two mainstream CGEC models, i.e., the sequence-to-sequence model and the sequence-to-edit model, both enhanced with large pretrained language models, 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{https://github.com/HillZhang1999/MuCGEC}.
