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}.
MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
A novel model, MISC, infers fine-grained user emotions and responds with a mixed strategy to reduce distress in emotional support conversations.
- Year
- 2022
- Venue
- ACL 2022 5
- Authors
- 6
- Hosting
- Abstract onlyARXIV-DEFAULT
Cite
Notes
Only stored in your browser.
Attribution
- Abstract & full text
- arxiv.org/abs/2203.13560v2ARXIV-DEFAULT
- TL;DR
- Semantic Scholar