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        <title>基于RoBERTa的中文仇恨言论侦测方法研究(Chinese Hate Speech detection method Based on RoBERTa-WWM)</title>
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    <abstract>“随着互联网的普及,社交媒体虽然提供了交流观点的平台,但因其虚拟性和匿名性也加剧了仇恨言论的传播,因此自动侦测仇恨言论对于维护社交媒体平台的文明发展至关重要。针对以上问题,构建了一个中文仇恨言论数据集CHSD,并提出了一种中文仇恨言论侦测模型RoBERTa-CHHSD。该模型首先采用RoBERTa预训练语言模型对中文仇恨言论进行序列化处理,提取文本特征信息;再分别接入TextCNN模型和Bi-GRU模型,提取多层次局部语义特征和句子间全局依赖关系信息;将二者结果融合来提取文本中更深层次的仇恨言论特征,对中文仇恨言论进行分类,从而实现中文仇恨言论的侦测。实验结果表明,本模型在CHSD数据集上的F1值为89.12%,与当前最优主流模型RoBERTa-WWM相比提升了1.76%。”</abstract>
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%0 Conference Proceedings
%T 基于RoBERTa的中文仇恨言论侦测方法研究(Chinese Hate Speech detection method Based on RoBERTa-WWM)
%A Rao, Xiaojun
%A Zhang, Yangsen
%A Jia, Qilong
%A Liu, Xueyang
%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
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G zho
%F rao-etal-2023-ji
%X “随着互联网的普及,社交媒体虽然提供了交流观点的平台,但因其虚拟性和匿名性也加剧了仇恨言论的传播,因此自动侦测仇恨言论对于维护社交媒体平台的文明发展至关重要。针对以上问题,构建了一个中文仇恨言论数据集CHSD,并提出了一种中文仇恨言论侦测模型RoBERTa-CHHSD。该模型首先采用RoBERTa预训练语言模型对中文仇恨言论进行序列化处理,提取文本特征信息;再分别接入TextCNN模型和Bi-GRU模型,提取多层次局部语义特征和句子间全局依赖关系信息;将二者结果融合来提取文本中更深层次的仇恨言论特征,对中文仇恨言论进行分类,从而实现中文仇恨言论的侦测。实验结果表明,本模型在CHSD数据集上的F1值为89.12%,与当前最优主流模型RoBERTa-WWM相比提升了1.76%。”
%U https://aclanthology.org/2023.ccl-1.44/
%P 501-511

Markdown (Informal)

[基于RoBERTa的中文仇恨言论侦测方法研究(Chinese Hate Speech detection method Based on RoBERTa-WWM)](https://aclanthology.org/2023.ccl-1.44/) (Rao et al., CCL 2023)

• 基于RoBERTa的中文仇恨言论侦测方法研究(Chinese Hate Speech detection method Based on RoBERTa-WWM) (Rao et al., CCL 2023)

ACL

• Xiaojun Rao, Yangsen Zhang, Qilong Jia, and Xueyang Liu. 2023. 基于RoBERTa的中文仇恨言论侦测方法研究(Chinese Hate Speech detection method Based on RoBERTa-WWM). In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 501–511, Harbin, China. Chinese Information Processing Society of China.