{"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/enriching-existing-conversational-emotion","title":"EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators","arxiv_id":"1912.00819","date":"2019-12-02","proceeding":"LREC 2020 5","authors":["Chandrakant Bothe","Cornelius Weber","Sven Magg","Stefan Wermter"],"abstract":"The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational emotion corpora contain only emotion labels but not dialogue acts. To address this problem, we propose to use a pool of various recurrent neural models trained on a dialogue act corpus, with and without context. These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label. We annotated two accessible multi-modal emotion corpora: IEMOCAP and MELD. We analyzed the co-occurrence of emotion and dialogue act labels and discovered specific relations. For example, Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy. We make the Emotional Dialogue Acts (EDA) corpus publicly available to the research community for further study and analysis.","url_abs":"https://arxiv.org/abs/1912.00819v3","url_pdf":"https://arxiv.org/pdf/1912.00819v3.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":"enriching-existing-conversational-emotion","repo_url":"https://github.com/bothe/EDAs","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"enriching-existing-conversational-emotion","repo_url":"https://github.com/bothe/dialogue-act-recognition","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"dialogue-act-classification","task_name":"Dialogue Act Classification"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-interpretation","task_name":"Emotion Interpretation"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"emotional-dialogue-acts","task_name":"Emotional Dialogue Acts"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"}],"methods":[{"method_slug":"deep-ensembles","method_name":"Deep Ensembles"},{"method_slug":"ensemble-clustering","method_name":"Ensemble Clustering"}],"datasets_introduced":[{"slug":"emotional-dialogue-acts","name":"Emotional Dialogue Acts","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.00819","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}