{"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/tsam-a-two-stream-attention-model-for-causal","title":"TSAM: A Two-Stream Attention Model for Causal Emotion Entailment","arxiv_id":"2203.00819","date":"2022-03-02","proceeding":"COLING 2022 10","authors":["Duzhen Zhang","Zhen Yang","Fandong Meng","Xiuyi Chen","Jie zhou"],"abstract":"Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between utterances in a global view and propose a Two-Stream Attention Model (TSAM) to effectively model the speaker's emotional influences in the conversational history. Specifically, the TSAM comprises three modules: Emotion Attention Network (EAN), Speaker Attention Network (SAN), and interaction module. The EAN and SAN incorporate emotion and speaker information in parallel, and the subsequent interaction module effectively interchanges relevant information between the EAN and SAN via a mutual BiAffine transformation. Extensive experimental results demonstrate that our model achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably.","url_abs":"https://arxiv.org/abs/2203.00819v2","url_pdf":"https://arxiv.org/pdf/2203.00819v2.pdf","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":"tsam-a-two-stream-attention-model-for-causal","repo_url":"https://github.com/bladedancer957/tsam","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"tsam-a-two-stream-attention-model-for-causal","repo_url":"https://github.com/MindSpore-scientific/code-1/tree/main/TSA_mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"tsam-a-two-stream-attention-model-for-causal","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/7/TSA_mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"causal-emotion-entailment","task_name":"Causal Emotion Entailment"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/causal-emotion-entailment-on-reccon","task":"Causal Emotion Entailment","dataset":"RECCON","model":"EAN","rank_in_archive_order":4,"of":9,"metrics":{"Macro F1":"80.24","Neg. F1":"90.48","Pos. F1":"70.00"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.00819","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}