Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
Ting Zhang, Zhuang Chen, Ming Zhong, Tieyun Qian
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
AI for Good. Pages 6299-6307.
https://doi.org/10.24963/ijcai.2023/699
Emotion recognition in conversation, which aims to predict the emotion for all utterances, has attracted considerable research attention in recent years. It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels. In this paper, we propose a novel framework which mimics the thinking process when modeling these factors. Specifically, we first comprehend the conversational context with a history-oriented prompt to selectively gather information from predecessors of the target utterance. We then model the speaker's background with an experience-oriented prompt to retrieve the similar utterances from all conversations. We finally differentiate the subtle label semantics with a paraphrasing mechanism to elicit the intrinsic label related knowledge.
We conducted extensive experiments on three benchmarks. The empirical results demonstrate the superiority of our proposed framework over the state-of-the-art baselines.
Keywords:
AI for Good: Natural Language Processing
AI for Good: Humans and AI