计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (2): 300-311.doi: 10.11896/jsjkx.250500015

• 人工智能 • 上一篇    下一篇

用于中英文讽刺检测的动态交互双通道图注意力网络

谭萍萍1,2, 徐计2, 李逸骏3, 汪海3   

  1. 1 贵州大学计算机科学与技术学院 贵阳 550025
    2 省部共建公共大数据国家重点实验室(贵州大学) 贵阳 550025
    3 贵州白山云科技股份有限公司 贵阳 550081
  • 收稿日期:2025-05-07 修回日期:2025-07-04 发布日期:2026-02-10
  • 通讯作者: 徐计(jixu@gzu.edu.cn)
  • 作者简介:(gs.pptan22@std.uestc.edu.cn)
  • 基金资助:
    国家自然科学基金(62366008,61966005)

Dynamic Interaction Dual-channel Graph Attention Network for Chinese and English SarcasmDetection

TAN Pingping1,2, XU Ji2, LI Yijun3, WANG Hai3   

  1. 1 College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
    2 State Key Laboratory of Public Big Data(Guizhou University),Guiyang 550025,China
    3 Baishan Cloud Technology Co.,Ltd.,Guiyang 550081,China
  • Received:2025-05-07 Revised:2025-07-04 Online:2026-02-10
  • About author:TAN Pingping,born in 1999,postgra-duate,is a student member of CCF(No.N8697G).Her main research in-terests include natural language processing and artificial intelligence.
    XU Ji,born in 1979.Ph.D,distinguished professor.His main research interests include data mining,granular computing and machine learning.
  • Supported by:
    National Natural Science Foundation of China(62366008,61966005).

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摘要： 汉语语义的复杂性和情感的细腻表达,导致中文文本讽刺检测具有挑战性。现有的讽刺检测方法多基于英文开发,难以适应中文的独特表达方式和文化内涵。因此,提出了一种新颖的动态交互双通道图注意力网络(Dynamic Interaction Dual-channel Graph Attention Network,DiDu-GAT),利用独特的双通道结构来分析文本中的句法依赖关系与情感特征。DiDu-GAT设计动态交互机制增强其跨通道学习能力,全面提取情感信息和句法信息,从而显著提升了中文讽刺检测的准确率。在哈工大中文讽刺数据集(GuanSarcasm)和两个公开英文讽刺数据集(IAC-V1和IAC-V2)上的实验结果表明,所提方法在主要性能指标上均显著优于现有基线方法,其在中英文讽刺检测任务中的有效性和优越性得到验证。

关键词: 图注意力网络, 中文讽刺检测, 双通道动态交互, 情感分析, 讽刺文本识别

Abstract: Due to the complexity of Chinese semantics and the nuanced expression of emotions,Chinese text sarcasm detection presents a challenging task.Existing sarcasm detection methods are predominantly developed for English and struggle to adapt to the unique expressions and cultural connotations of Chinese.Therefore,this paper proposes a novel dynamic interaction dual-channel graph attention network(DiDu-GAT),which utilizes a unique dual-channel structure to analyze syntactic dependencies and emotional features in texts.DiDu-GAT incorporates a dynamic interaction mechanism to enhance its cross-channel learning capabilities,enabling comprehensive extraction of emotional information and syntactic patterns,thereby significantly improving the accuracy of Chinese sarcasm detection.Experimental results on the HIT Chinese sarcasm dataset(GuanSarcasm) and two public English sarcasm datasets(IAC-V1 and IAC-V2) demonstrate that the proposed method significantly outperforms existing baseline methods across key performance metrics,validating its effectiveness and superiority in both Chinese and English sarcasm detection tasks.

Key words: Graph attention network, Chinese sarcasm detection, Dual-channel dynamic interaction, Sentiment analysis, Sarcasm text recognition

中图分类号: 

  • TP311.13
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