{"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/top-down-discourse-parsing-via-sequence","title":"Top-down Discourse Parsing via Sequence Labelling","arxiv_id":"2102.02080","date":"2021-02-03","proceeding":"EACL 2021 2","authors":["Fajri Koto","Jey Han Lau","Timothy Baldwin"],"abstract":"We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points. We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing. Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing.","url_abs":"https://arxiv.org/abs/2102.02080v2","url_pdf":"https://arxiv.org/pdf/2102.02080v2.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":"top-down-discourse-parsing-via-sequence","repo_url":"https://github.com/fajri91/NeuralRST-TopDown","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"discourse-parsing","task_name":"Discourse Parsing"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/discourse-parsing-on-rst-dt","task":"Discourse Parsing","dataset":"RST-DT","model":"LSTM Dynamic","rank_in_archive_order":16,"of":40,"metrics":{"Standard Parseval (Full)":"50.3","Standard Parseval (Nuclearity)":"62.3","Standard Parseval (Relation)":"51.5","Standard Parseval (Span)":"73.1"},"uses_additional_data":false},{"leaderboard":"/sota/discourse-parsing-on-rst-dt","task":"Discourse Parsing","dataset":"RST-DT","model":"LSTM Static","rank_in_archive_order":18,"of":40,"metrics":{"Standard Parseval (Full)":"49.4","Standard Parseval (Nuclearity)":"61.7","Standard Parseval (Relation)":"50.5","Standard Parseval (Span)":"72.7"},"uses_additional_data":false},{"leaderboard":"/sota/discourse-parsing-on-rst-dt","task":"Discourse Parsing","dataset":"RST-DT","model":"Transformer (dynamic)","rank_in_archive_order":19,"of":40,"metrics":{"Standard Parseval (Full)":"49.2","Standard Parseval (Nuclearity)":"60.1","Standard Parseval (Span)":"70.2"},"uses_additional_data":false},{"leaderboard":"/sota/discourse-parsing-on-rst-dt","task":"Discourse Parsing","dataset":"RST-DT","model":"Transformer (static)","rank_in_archive_order":20,"of":40,"metrics":{"Standard Parseval (Full)":"49.0","Standard Parseval (Nuclearity)":"59.9","Standard Parseval (Relation)":"50.6","Standard Parseval (Span)":"70.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}