@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",
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 $\textbf{MISC}$, 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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation
%A Tu, Quan
%A Li, Yanran
%A Cui, Jianwei
%A Wang, Bin
%A Wen, Ji-Rong
%A Yan, Rui
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F tu-etal-2022-misc
%X 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.
%R 10.18653/v1/2022.acl-long.25
%U https://aclanthology.org/2022.acl-long.25
%U https://doi.org/10.18653/v1/2022.acl-long.25
%P 308-319
Markdown (Informal)
[MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation](https://aclanthology.org/2022.acl-long.25) (Tu et al., ACL 2022)
ACL