{"type":"ScholarlyArticle","title":"ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains","authors":[{"name":"Zhaopei Huang","slug":"zhaopei-huang","given":"Zhaopei","family":"Huang","affiliation":"Renmin University of China"},{"name":"Jinming Zhao","slug":"jinming-zhao","given":"Jinming","family":"Zhao","affiliation":"Independent Researcher"},{"name":"Qin Jin","slug":"qin-jin","given":"Qin","family":"Jin","affiliation":"Renmin University of China"}],"abstract":"Understanding the process of emotion generation is crucial for analyzing the causes behind emotions. Causal Emotion Entailment (CEE), an emotion-understanding task, aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. However, current works in CEE mainly focus on modeling semantic and emotional interactions in conversations, neglecting the exploration of the emotion-generation process. This hinders the models from deeply understanding emotions, restricting their ability to produce explainable predictions. In this work, inspired by the emotion generation process of \"stimulus-appraisal-emotion\" in the cognitive appraisal theory, we introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations. Specifically, we first introduce the ECR-Chain to ChatGPT via few-shot prompting, which significantly improves its performance on the CEE task. We further propose an automated construction process to utilize ChatGPT in building an ECR-Chain set, which can enhance the reasoning abilities of smaller models through supervised training and assist the Vicuna-7B model in achieving state-of-the-art CEE performance. Moreover, our methods can enable these generative language models to effectively perform emotion-cause reasoning in an explainable manner. Our code, data and more details are at https://github.com/hzp3517/ECR-Chain.","doi":"10.24963/ijcai.2024/695","url":"https://ijcai.org/proceedings/2024/695","pdf":"https://ijcai.org/proceedings/2024/0695.pdf","markdown":"https://ijcai.org/proceedings/2024/695.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/695","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":695,"pages":{"first":6288,"last":6296},"keywords":["Natural Language Processing: NLP: Sentiment analysis, stylistic analysis, and argument mining"],"videos":[],"references":[{"position":1,"raw":"[Arnold, 1960] Magda B Arnold. 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