TY  - EJOU
AU  - Xing, Xinlai 
AU  - Chen, Junliang 
AU  - Zhang, Xiaochuan 
AU  - Zhou, Shuran 
AU  - Zhang, Runqing 

TI  - Multi-Head Encoder Shared Model Integrating Intent and Emotion for Dialogue Summarization
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Dialogue summaries; dialogue state tracking; emotion recognition; task-oriented dialogue system; pre-trained language model

DO  - 10.32604/cmc.2024.056877