Multiple Information-Aware Recurrent Reasoning Network for Joint Dialogue Act Recognition and Sentiment Classification

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shi Li, Xiaoting Chen
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引用次数: 0

Abstract

The task of joint dialogue act recognition (DAR) and sentiment classification (DSC) aims to predict both the act and sentiment labels of each utterance in a dialogue. Existing methods mainly focus on local or global semantic features of the dialogue from a single perspective, disregarding the impact of the other part. Therefore, we propose a multiple information-aware recurrent reasoning network (MIRER). Firstly, the sequence information is smoothly sent to multiple local information layers for fine-grained feature extraction through a BiLSTM-connected hybrid CNN group method. Secondly, to obtain global semantic features that are speaker-, context-, and temporal-sensitive, we design a speaker-aware temporal reasoning heterogeneous graph to characterize interactions between utterances spoken by different speakers, incorporating different types of nodes and meta-relations with node-edge-type-dependent parameters. We also design a dual-task temporal reasoning heterogeneous graph to realize the semantic-level and prediction-level self-interaction and interaction, and we constantly revise and improve the label in the process of dual-task recurrent reasoning. MIRER fully integrates context-level features, fine-grained features, and global semantic features, including speaker, context, and temporal sensitivity, to better simulate conversation scenarios. We validated the method on two public dialogue datasets, Mastodon and DailyDialog, and the experimental results show that MIRER outperforms various existing baseline models.
联合对话行为识别与情感分类的多信息感知循环推理网络
联合对话行为识别(DAR)和情感分类(DSC)的任务旨在预测对话中每个话语的行为和情感标签。现有的方法主要是从单一角度关注对话的局部或全局语义特征,而忽略了另一部分的影响。因此,我们提出了一种多信息感知循环推理网络(MIRER)。首先,通过bilstm连接的混合CNN群方法,将序列信息平滑发送到多个局部信息层进行细粒度特征提取;其次,为了获得说话人、上下文和时间敏感的全局语义特征,我们设计了一个说话人感知的时间推理异构图来表征不同说话人所说的话语之间的相互作用,并结合了不同类型的节点和元关系以及节点边缘类型依赖的参数。我们还设计了双任务时间推理异构图,实现语义级和预测级的自交互和交互,并在双任务循环推理过程中不断修改和完善标签。MIRER完全集成了上下文级功能、细粒度功能和全局语义功能,包括说话人、上下文和时间敏感性,以更好地模拟会话场景。我们在两个公共对话数据集Mastodon和DailyDialog上验证了该方法,实验结果表明,MIRER优于现有的各种基线模型。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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