MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
This work addresses emotional support for people in need, but it appears incremental as it builds on existing methods by adding fine-grained emotion understanding and mixed strategies.
The paper tackles the problem of emotional support conversation by addressing coarse-grained emotion labels and lack of distress reduction, proposing the MISC model that infers fine-grained emotional status and uses mixed strategies, with experimental results demonstrating its effectiveness on a benchmark dataset.
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. Our code and data could be found in \url{https://github.com/morecry/MISC}.