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. Our code and data could be found in https://github.com/morecry/MISC.
CITATION STYLE
Tu, Q., Li, Y., Cui, J., Wang, B., Wen, J. R., & Yan, R. (2022). MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 308–319). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.25
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