MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

TL;DR

This paper introduces MISC, a model for emotional support conversations that infers fine-grained user emotions and employs mixed strategies to respond more effectively, improving emotional understanding and response quality.

Contribution

MISC is the first model to combine fine-grained emotion inference with mixed strategy response generation in emotional support conversations.

Findings

Outperforms existing methods on benchmark datasets

Demonstrates the importance of fine-grained emotion understanding

Shows benefits of mixed strategy modeling

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. Our code and data could be found in \url{https://github.com/morecry/MISC}.