{"type":"ScholarlyArticle","title":"Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation","authors":[{"name":"Yinan Bao","slug":"yinan-bao","given":"Yinan","family":"Bao","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences {baoyinan ,"},{"name":"Qianwen Ma","slug":"qianwen-ma","given":"Qianwen","family":"Ma","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences {baoyinan ,"},{"name":"Lingwei Wei","slug":"lingwei-wei","given":"Lingwei","family":"Wei","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences {baoyinan ,"},{"name":"Wei Zhou","slug":"wei-zhou","given":"Wei","family":"Zhou","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences"},{"name":"Songlin Hu","slug":"songlin-hu","given":"Songlin","family":"Hu","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences {baoyinan ,"}],"abstract":"The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.","doi":"10.24963/ijcai.2022/562","url":"https://ijcai.org/proceedings/2022/562","pdf":"https://ijcai.org/proceedings/2022/0562.pdf","markdown":"https://ijcai.org/proceedings/2022/562.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/562","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Natural Language Processing","track":"Main Track","number":562,"pages":{"first":4051,"last":4057},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: Applications","Natural Language Processing: Text Classification"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986618","length_sec":85,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984761","length_sec":338,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Busso et al., 2008] Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N. 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