| 摘要: |
| 在线讨论是当下公众表达意见和交流互动的主要方式之一. 参与者不仅发布评论来表述自己的观点, 还会回复已有的表述进行应答, 支持或反驳他人的观点. 识别表述-应答交互文本的论辩关系可以建模公众对话结构, 挖掘群体意见, 进而为企业产品营销、政府舆情监测等提供辅助. 现有的工作大多通过神经网络对交互文本的条件语义信息或者整体语义信息进行建模, 而忽略了交互文本的语境关联信息. 为此, 提出了一种挖掘语境关联的双向注意力网络模型(CCRnet). 该模型使用BERT分别对表述和应答进行文本语义表示, 并通过双向注意力机制建模交互文本的语境关联表示. 在此基础上, 模型将语境关联表示和交互文本的语义表示进行融合, 建模全局关系特征, 继而输出预测标签. 在CreateDebate数据集上的实验结果表明, 与目前主流的方法相比, CCRnet模型的整体性能表现更优. 此外, 可视化相似度矩阵证实, 双向注意力机制能够有效捕捉交互文本之间的语境关联信息并进一步服务于论辩关系预测. |
| 关键词: 机器学习 深度学习 论辩关系预测 注意力机制 |
| DOI：10.13328/j.cnki.jos.006320 |
| 分类号: |
| 基金项目:国家自然科学基金面上项目(62172094); 对外经济贸易大学中央高校基本科研业务费专项资金(CXTD13-01); 对外经济贸易大学研究生科研创新基金(202150) |
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| Predicting Argumentative Relation with Co-attention Contextual Relevance Network |
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SHAN Hua-Wei, LU Dong-Yuan
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School of Information Technology & Management, University of International Business and Economics, Beijing 100029, China
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| Abstract: |
| Online discussion has become a main way for people to communicate opinions. Besides posting statements, users are also encouraged to reply to existing posts, revealing support or disapproval of others' viewpoints. Identifying argumentative relations between these interactive texts can benefit modeling the dialogue structure, detecting public opinions, and supporting business, marketing, and government to make decisions. Existing studies detected argumentative relations by constructing overall semantic information or conditional semantic information, but the contextual relevance information between interactive texts was ignored. This work proposed a co-attention contextual relevance network (CCRnet). With the co-attention mechanism, the model captured bi-directional attention between the post and reply. Experimental results on the CreateDebate dataset show that he proposed model outperforms the state-of-the-art models. Furthermore, the visualization of the similarity matrix illustrates the effectiveness of the co-attention mechanism. |
| Key words: machine learning deep learning argumentative relation prediction attention mechanism |
