@inproceedings{yu-etal-2023-ccl23-eval,
title = "{CCL}23-Eval 任务7系统报告:基于序列标注和指针生成网络的语法纠错方法(System Report for {CCL}23-Eval Task 7:A Syntactic Error Correction Approach Based on Sequence Labeling and Pointer Generation Networks)",
author = "Yu, Youren and
Zhang, Yangsen and
Chang, Guanguang and
Gao, Beibei and
Jiang, Yushan and
Xiao, Tuo",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-3.26",
pages = "230--238",
abstract = "{``}针对当前大多数中文语法纠错模型存在错误边界识别不准确以及过度纠正的问题,我们提出了一种基于序列标注与指针生成网络的中文语法纠错模型。首先,在数据方面,我们使用了官方提供的lang8数据集和历年的CGED数据集,并对该数据集进行了繁体转简体、数据清洗等操作。其次,在模型方面,我们采用了ERNIE+Global Pointer的序列标注模型、基于ERNIE+CRF的序列标注模型、基于BART+指针生成网络的纠错模型以及基于CECToR的纠错模型。最后,在模型集成方面,我们使用了投票和基于ERNIE模型计算困惑度的方法,来生成最终预测结果。根据测试集的结果,我们的乃乏乍指标达到了48.68,位居第二名。{''}",
language = "Chinese",
}
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<title>CCL23-Eval 任务7系统报告:基于序列标注和指针生成网络的语法纠错方法(System Report for CCL23-Eval Task 7:A Syntactic Error Correction Approach Based on Sequence Labeling and Pointer Generation Networks)</title>
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<abstract>“针对当前大多数中文语法纠错模型存在错误边界识别不准确以及过度纠正的问题,我们提出了一种基于序列标注与指针生成网络的中文语法纠错模型。首先,在数据方面,我们使用了官方提供的lang8数据集和历年的CGED数据集,并对该数据集进行了繁体转简体、数据清洗等操作。其次,在模型方面,我们采用了ERNIE+Global Pointer的序列标注模型、基于ERNIE+CRF的序列标注模型、基于BART+指针生成网络的纠错模型以及基于CECToR的纠错模型。最后,在模型集成方面,我们使用了投票和基于ERNIE模型计算困惑度的方法,来生成最终预测结果。根据测试集的结果,我们的乃乏乍指标达到了48.68,位居第二名。”</abstract>
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%0 Conference Proceedings
%T CCL23-Eval 任务7系统报告:基于序列标注和指针生成网络的语法纠错方法(System Report for CCL23-Eval Task 7:A Syntactic Error Correction Approach Based on Sequence Labeling and Pointer Generation Networks)
%A Yu, Youren
%A Zhang, Yangsen
%A Chang, Guanguang
%A Gao, Beibei
%A Jiang, Yushan
%A Xiao, Tuo
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F yu-etal-2023-ccl23-eval
%X “针对当前大多数中文语法纠错模型存在错误边界识别不准确以及过度纠正的问题,我们提出了一种基于序列标注与指针生成网络的中文语法纠错模型。首先,在数据方面,我们使用了官方提供的lang8数据集和历年的CGED数据集,并对该数据集进行了繁体转简体、数据清洗等操作。其次,在模型方面,我们采用了ERNIE+Global Pointer的序列标注模型、基于ERNIE+CRF的序列标注模型、基于BART+指针生成网络的纠错模型以及基于CECToR的纠错模型。最后,在模型集成方面,我们使用了投票和基于ERNIE模型计算困惑度的方法,来生成最终预测结果。根据测试集的结果,我们的乃乏乍指标达到了48.68,位居第二名。”
%U https://aclanthology.org/2023.ccl-3.26
%P 230-238
Markdown (Informal)
[CCL23-Eval 任务7系统报告:基于序列标注和指针生成网络的语法纠错方法(System Report for CCL23-Eval Task 7:A Syntactic Error Correction Approach Based on Sequence Labeling and Pointer Generation Networks)](https://aclanthology.org/2023.ccl-3.26) (Yu et al., CCL 2023)
ACL