A Study of empathic dialogue generation based on affective intention prediction

Yulin Feng; Bin Wei; Hongzhi Yu; Fucheng Wan; Min Chen; Baoqing Lu

doi:10.1117/12.3038744

7 August 2024 A Study of empathic dialogue generation based on affective intention prediction

Yulin Feng, Bin Wei, Hongzhi Yu, Fucheng Wan, Min Chen, Baoqing Lu

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132293E (2024) https://doi.org/10.1117/12.3038744
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Aiming at the problems of lack of emotional resonance and unsatisfactory anthropomorphic effect in the responses generated by the existing dialog models, a dialog generation model based on the prediction of emotional intent is proposed. The model predicts the intention information of the context to obtain fine-grained emotion information to construct a high-quality dialog generation system, combines with an emotion classifier to identify the emotion category of the input utterance, and generates corresponding empathetic replies based on the emotion comparison mechanism according to the different intention information and emotion information. The experimental results show that compared with the traditional dialog generation model, the proposed model can recognize the user's emotional intent and emotion information, generate responses that are more in line with the user's expectations and adapt to the context, and realize a more empathetic emotional dialog process.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yulin Feng, Bin Wei, Hongzhi Yu, Fucheng Wan, Min Chen, and Baoqing Lu "A Study of empathic dialogue generation based on affective intention prediction", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132293E (7 August 2024); https://doi.org/10.1117/12.3038744

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