Multi-Head Encoder Shared Model Integrating Intent and Emotion for Dialogue Summarization  

作　　者：Xinlai Xing Junliang Chen Xiaochuan Zhang Shuran Zhou Runqing Zhang 

机构地区：[1]School of Artificial Intelligence,Chongqing University of Technology,Chongqing,401135,China

出　　处：《Computers, Materials & Continua》2025年第2期2275-2292,共18页计算机、材料和连续体(英文)

基　　金：funded by the Science and Technology Foundation of Chongqing EducationCommission(GrantNo.KJQN202301153);the ScientificResearch Foundation of Chongqing University of Technology(Grant No.2021ZDZ025);the Postgraduate Innovation Foundation of Chongqing University of Technology(Grant No.gzlcx20243524).

摘　　要：In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.

关 键 词：Dialogue summaries dialogue state tracking emotion recognition task-oriented dialogue system pre-trained language model 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]