北京大学学报自然科学版 ›› 2023, Vol. 59 ›› Issue (1): 21-29.DOI: 10.13209/j.0479-8023.2022.069

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基于多信息感知的多方对话问答方法

高晓倩, 周夏冰†, 张民   

  1. 苏州大学计算机科学与技术学院, 苏州 215000
  • 收稿日期:2022-05-13 修回日期:2022-08-05 出版日期:2023-01-20 发布日期:2023-01-20
  • 通讯作者: 周夏冰, E-mail: zhouxiabing(at)suda.edu.cn
  • 基金资助:
    国家自然科学基金(62176174)和江苏高校优势学科建设工程项目资助

A Multi-information Perception Based Method for Question Answering in Multi-party Conversation

GAO Xiaoqian, ZHOU Xiabing†, ZHANG Min   

  1. School of Computer Science and Technology, Soochow University, Suzhou 215000
  • Received:2022-05-13 Revised:2022-08-05 Online:2023-01-20 Published:2023-01-20
  • Contact: ZHOU Xiabing, E-mail: zhouxiabing(at)suda.edu.cn

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摘要：

目前基于多方对话文本的自动问答任务侧重于探索对话结构信息或说话者角色信息, 忽视问题文本和对话文本的交互。针对这一问题, 提出一个融合多信息的全新模型。该模型使用图卷积神经网络, 对多方对话文本中的话语结构、说话者角色以及问题–上下文信息进行分层次建模, 并设计合理的基于注意力机制的交互层, 通过选择更有帮助的信息, 加强对多方对话文本的理解。此外, 该模型首次对问题和上下文间的显式交互给予关注。实验结果表明, 所提模型的性能优于多个基线模型, 实现对多方对话文本的深层次理解。

关键词: 多方对话, 自动问答, 图卷积网络, 注意力机制

Abstract:

Question answering in multi-party conversation typically focuses on exploring discourse structures or speaker-aware information but ignores the interaction between questions and conversations. To solve this problem, a new model which integrates various information is proposed. In detail, to hierarchically model the discourse structures, speaker-aware dependency of interlocutors and question-context information, the proposed model leverages above information to propagate contextual information, by exploiting graph convolutional neural network. Besides, the model employs a reasonable interaction layer based on attention mechanism to enhance the understanding of multi-party conversations by selecting more helpful information. Furthermore, the model is the first to pay attention to the explicit interaction between question and context. The experimental results show that the model outperforms multiple baselines, illustrating that the model can understand the conversations more comprehensively.

Key words: multi-party in conversation, question answering, graph convolutional network, attention mechanism

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