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清华大学学报(自然科学版)  2021, Vol. 61 Issue (9): 913-919    DOI: 10.16511/j.cnki.qhdxxb.2021.21.006
  计算语言学 本期目录 | 过刊浏览 | 高级检索 |
基于循环交互注意力网络的问答立场分析
骆旺达, 刘宇瀚, 梁斌, 徐睿峰
哈尔滨工业大学(深圳) 计算机科学与技术学院, 深圳 518055
Answer stance detection based on recurrent interactive attention network
LUO Wangda, LIU Yuhan, LIANG Bin, XU Ruifeng
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
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摘要 针对现有问答立场分析方法未考虑问答文本间交互依赖关系的不足,该文提出一种基于循环交互注意力(recurrent interactive attention,RIA)网络的问答立场分析方法。该方法模拟人类的问答阅读理解机制,采用交互注意力机制和循环迭代策略,结合问题和回答的相互联系分析问答文本的立场信息。此外,为了处理问题文本无法明确表达自身立场的情况,该方法将问题转换为陈述句。在中文社交问答数据集上的实验结果表明,由于有效地表示了问答对依赖关系,本文方法的性能优于已有方法。
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骆旺达
刘宇瀚
梁斌
徐睿峰
关键词 问答立场分析循环交互注意力问题表示    
Abstract:Most existing answer stance detection methods ignore the interactive dependence between question and answer. This paper describes an answer stance detection method based on a recurrent interactive attention (RIA) network. This method simulates the human interactions in question-answer reading comprehension using an interactive attention mechanism and iterations to simulate the interactive dependence between question and answer for detecting the answer stance. In addition, since the question text cannot explicitly express its stance, the question text is transformed into declarative sentences. Tests on a Chinese social media question-answer dataset show that this method outperforms existing answer stance detection methods due to the effective representation of the interactive dependence between the question and the answer.
Key wordsanswer stance detection    recurrent interactive attention    question representation
收稿日期: 2020-11-28      出版日期: 2021-08-21
基金资助:国家自然科学基金面上项目(61876053);国家自然科学基金重点项目(61632011);深圳市基础研究学科布局项目(JCYJ20180507183527919,JCYJ20180507183608379)
通讯作者: 徐睿峰,教授,E-mail:xuruifeng@hit.edu.cn     E-mail: xuruifeng@hit.edu.cn
引用本文:   
骆旺达, 刘宇瀚, 梁斌, 徐睿峰. 基于循环交互注意力网络的问答立场分析[J]. 清华大学学报(自然科学版), 2021, 61(9): 913-919.
LUO Wangda, LIU Yuhan, LIANG Bin, XU Ruifeng. Answer stance detection based on recurrent interactive attention network. Journal of Tsinghua University(Science and Technology), 2021, 61(9): 913-919.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2021.21.006  或          http://jst.tsinghuajournals.com/CN/Y2021/V61/I9/913
  
  
  
  
  
  
  
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