{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mpeg-a-multi-perspective-enhanced-graph","title":"MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations","arxiv_id":null,"date":"2023-09-15","proceeding":"IEEE Transactions on Affective Computing 2023 9","authors":["Tiantian Chen","Ying Shen","Xuri Chen","Lin Zhang","Shengjie Zhao"],"abstract":"Emotion causes constitute a pivotal component in the comprehension of emotional conversations. Recently, a new task named Causal Emotion Entailment (CEE) has been proposed to identify the causal utterances for the target emotional utterance in a conversation. Although researchers have achieved some progress in solving this problem, they failed to adequately incorporate speaker characteristics and overlooked the effects of temporal relations in conversation structures. To fill such a research gap to some extent, we propose a novel causal emotion entailment framework, namely MPEG (Multi-Perspective Enhanced Graph attention network). The training of MPEG consists of three stages. First, we utilize a speaker-aware pre-trained model and two attention mechanisms to obtain the utterance representations that incorporate local contexts as well as the speaker and emotional information. Then, these representations are fed into a graph attention network to model the conversation structures and emotional dynamics from both local and global perspectives. Finally, a fully-connected network is implemented to predict the relationships between emotional utterances and causal utterances. Experimental results show that MPEG achieves state-of-the-art performance.","url_abs":"https://ieeexplore.ieee.org/document/10252019","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10252019","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mpeg-a-multi-perspective-enhanced-graph","repo_url":"https://github.com/slptongji/MPEG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"causal-emotion-entailment","task_name":"Causal Emotion Entailment"},{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"graph-attention","task_name":"Graph Attention"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/causal-emotion-entailment-on-reccon","task":"Causal Emotion Entailment","dataset":"RECCON","model":"MPEG","rank_in_archive_order":2,"of":9,"metrics":{"Macro F1":"80.76","Neg. F1":"90.35","Pos. F1":"71.18"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}