{"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/dialogue-act-classification-with-context","title":"Dialogue Act Classification with Context-Aware Self-Attention","arxiv_id":"1904.02594","date":"2019-04-04","proceeding":"NAACL 2019 6","authors":["Vipul Raheja","Joel Tetreault"],"abstract":"Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.","url_abs":"https://arxiv.org/abs/1904.02594v2","url_pdf":"https://arxiv.org/pdf/1904.02594v2.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":"dialogue-act-classification-with-context","repo_url":"https://github.com/macabdul9/CASA-Dialogue-Act-Classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"dialogue-act-classification","task_name":"Dialogue Act Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/dialogue-act-classification-on-icsi-meeting","task":"Dialogue Act Classification","dataset":"ICSI Meeting Recorder Dialog Act (MRDA) corpus","model":"Bi-RNN + Self-Attention + Context","rank_in_archive_order":6,"of":8,"metrics":{"Accuracy":"91.1"},"uses_additional_data":false},{"leaderboard":"/sota/dialogue-act-classification-on-switchboard","task":"Dialogue Act Classification","dataset":"Switchboard corpus","model":"Bi-RNN + Self-Attention + Context","rank_in_archive_order":3,"of":11,"metrics":{"Accuracy":"82.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.02594","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}