{"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/structured-dialogue-discourse-parsing-1","title":"Structured Dialogue Discourse Parsing","arxiv_id":"2306.15103","date":"2023-06-26","proceeding":"SIGDIAL (ACL) 2022 9","authors":["Ta-Chung Chi","Alexander I. Rudnicky"],"abstract":"Dialogue discourse parsing aims to uncover the internal structure of a multi-participant conversation by finding all the discourse~\\emph{links} and corresponding~\\emph{relations}. Previous work either treats this task as a series of independent multiple-choice problems, in which the link existence and relations are decoded separately, or the encoding is restricted to only local interaction, ignoring the holistic structural information. In contrast, we propose a principled method that improves upon previous work from two perspectives: encoding and decoding. From the encoding side, we perform structured encoding on the adjacency matrix followed by the matrix-tree learning algorithm, where all discourse links and relations in the dialogue are jointly optimized based on latent tree-level distribution. From the decoding side, we perform structured inference using the modified Chiu-Liu-Edmonds algorithm, which explicitly generates the labeled multi-root non-projective spanning tree that best captures the discourse structure. In addition, unlike in previous work, we do not rely on hand-crafted features; this improves the model's robustness. Experiments show that our method achieves new state-of-the-art, surpassing the previous model by 2.3 on STAC and 1.5 on Molweni (F1 scores). \\footnote{Code released at~\\url{https://github.com/chijames/structured_dialogue_discourse_parsing}.}","url_abs":"https://arxiv.org/abs/2306.15103v1","url_pdf":"https://arxiv.org/pdf/2306.15103v1.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":"structured-dialogue-discourse-parsing-1","repo_url":"https://github.com/chijames/structured_dialogue_discourse_parsing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"discourse-parsing","task_name":"Discourse Parsing"},{"task_slug":"multiple-choice","task_name":"Multiple-choice"}],"methods":[{"method_slug":"stac","method_name":"STAC"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/discourse-parsing-on-molweni","task":"Discourse Parsing","dataset":"Molweni","model":"Structured","rank_in_archive_order":2,"of":7,"metrics":{"Link & Rel F1":"59.9","Link F1":"83.5"},"uses_additional_data":false},{"leaderboard":"/sota/discourse-parsing-on-stac","task":"Discourse Parsing","dataset":"STAC","model":"Structured","rank_in_archive_order":1,"of":7,"metrics":{"Link & Rel F1":"59.6","Link F1":"74.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}