{"type":"ScholarlyArticle","title":"Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing","authors":[{"name":"Ting Zhang","slug":"ting-zhang","given":"Ting","family":"Zhang","affiliation":"School of Computer Science , Wuhan University"},{"name":"Zhuang Chen","slug":"zhuang-chen","given":"Zhuang","family":"Chen","affiliation":"The CoAI group , DCST , Tsinghua University"},{"name":"Ming Zhong","slug":"ming-zhong","given":"Ming","family":"Zhong","affiliation":"School of Computer Science , Wuhan University"},{"name":"Tieyun Qian","slug":"tieyun-qian","given":"Tieyun","family":"Qian","affiliation":"School of Computer Science , Wuhan University; Intellectual Computing Laboratory for Cultural Heritage , Wuhan University"}],"abstract":"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.\r\nWe conducted extensive experiments on three benchmarks. The empirical results demonstrate the superiority of our proposed framework over the state-of-the-art baselines.","doi":"10.24963/ijcai.2023/699","url":"https://ijcai.org/proceedings/2023/699","pdf":"https://ijcai.org/proceedings/2023/0699.pdf","markdown":"https://ijcai.org/proceedings/2023/699.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/699","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":699,"pages":{"first":6299,"last":6307},"keywords":["AI for Good: Natural Language Processing","AI for Good: Humans and AI"],"videos":[],"references":[{"position":1,"raw":"[Baccianella et al., 2010] Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 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