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.- Anthology ID:
- 2024.findings-acl.577
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9689–9706
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.577
- DOI:
- 10.18653/v1/2024.findings-acl.577
- Cite (ACL):
- Elena Chistova. 2024. Bilingual Rhetorical Structure Parsing with Large Parallel Annotations. In Findings of the Association for Computational Linguistics: ACL 2024, pages 9689–9706, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Bilingual Rhetorical Structure Parsing with Large Parallel Annotations (Chistova, Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.577.pdf