{"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/bilingual-rhetorical-structure-parsing-with","title":"Bilingual Rhetorical Structure Parsing with Large Parallel Annotations","arxiv_id":"2409.14969","date":"2024-09-23","proceeding":null,"authors":["Elena Chistova"],"abstract":"Discourse parsing is a crucial task in natural language processing that aims to reveal the higher-level relations in a text. Despite growing interest in cross-lingual discourse parsing, challenges persist due to limited parallel data and inconsistencies in the Rhetorical Structure Theory (RST) application across languages and corpora. To address this, we introduce a parallel Russian annotation for the large and diverse English GUM RST corpus. Leveraging recent advances, our end-to-end RST parser achieves state-of-the-art results on both English and Russian corpora. It demonstrates effectiveness in both monolingual and bilingual settings, successfully transferring even with limited second-language annotation. To the best of our knowledge, this work is the first to evaluate the potential of cross-lingual end-to-end RST parsing on a manually annotated parallel corpus.","url_abs":"https://arxiv.org/abs/2409.14969v1","url_pdf":"https://arxiv.org/pdf/2409.14969v1.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":"bilingual-rhetorical-structure-parsing-with","repo_url":"https://github.com/tchewik/bilingualrsp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"discourse-parsing","task_name":"Discourse Parsing"},{"task_slug":"end-to-end-rst-parsing","task_name":"End-to-End RST Parsing"}],"methods":[],"datasets_introduced":[{"slug":"rrg","name":"RRG","full_name":"Russian RST dataset from GUM v9.1 corpus"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/discourse-parsing-on-rst-dt","task":"Discourse Parsing","dataset":"RST-DT","model":"DMRST","rank_in_archive_order":5,"of":40,"metrics":{"Standard Parseval (Full)":"55.7 ± 0.3","Standard Parseval (Nuclearity)":"68.0 ± 0.6","Standard Parseval (Relation)":"57.3 ± 0.2","Standard Parseval (Span)":"78.7 ± 0.4"},"uses_additional_data":false},{"leaderboard":"/sota/end-to-end-rst-parsing-on-rst-dt-1","task":"End-to-End RST Parsing","dataset":"RST-DT","model":"DMRST  + ToNy + E-BiLSTM","rank_in_archive_order":1,"of":4,"metrics":{"Standard Parseval (Full)":"53.0 ± 0.7","Standard Parseval (Nuclearity)":"64.5 ± 0.8","Standard Parseval (Relation)":"54.5 ± 0.7","Standard Parseval (Span)":"74.8 ± 0.5"},"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}