An Empirical Study on Multiple Knowledge from ChatGPT for Emotion Recognition in Conversations
Geng Tu, Bin Liang, Bing Qin, Kam-Fai Wong, Ruifeng Xu
Abstract
Multiple knowledge (e.g., co-reference, topics, emotional causes, etc) has been demonstrated effective for emotion detection. However, exploring this knowledge in Emotion Recognition in Conversations (ERC) is currently a blank slate due to the lack of annotated data and the high cost involved in obtaining such knowledge. Fortunately, the emergence of Large Language Models (LLMs) holds promise in filling this void. Therefore, we propose a Multiple Knowledge Fusion Model (MKFM) to effectively integrate such knowledge generated by LLMs for ERC and empirically study its impact on the model. Experimental results on three public datasets have demonstrated the effectiveness of multiple knowledge for ERC. Furthermore, we conduct a detailed analysis of the contribution and complementarity of this knowledge.
BibTeX
@inproceedings{
tu2023an,
title={An Empirical Study on Multiple Knowledge from Chat{GPT} for Emotion Recognition in Conversations},
author={Geng Tu and Bin Liang and Bing Qin and Kam-Fai Wong and Ruifeng Xu},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=F4qNZtkk3V}
}