MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation
Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, Rui Yan
Abstract
Applying existing methods to emotional support conversation—which provides valuable assistance to people who are in need—has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user’s instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user’s distress. To address the problems, we propose a novel model MISC, which firstly infers the user’s fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling.
BibTeX
@inproceedings{tu-etal-2022-misc,
title = "{MISC}: A Mixed Strategy-Aware Model integrating {COMET} for Emotional Support Conversation",
author = "Tu, Quan and
Li, Yanran and
Cui, Jianwei and
Wang, Bin and
Wen, Ji-Rong and
Yan, Rui",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.25/",
doi = "10.18653/v1/2022.acl-long.25",
pages = "308--319"
}