A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
Xiang Lin, Shafiq Joty, Prathyusha Jwalapuram, M Saiful Bari
Abstract
We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an F1 score of 95.4%, and our parser achieves an F1 score of 81.7% on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 F1).- Anthology ID:
- P19-1410
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4190–4200
- Language:
- URL:
- https://aclanthology.org/P19-1410
- DOI:
- 10.18653/v1/P19-1410
- Cite (ACL):
- Xiang Lin, Shafiq Joty, Prathyusha Jwalapuram, and M Saiful Bari. 2019. A Unified Linear-Time Framework for Sentence-Level Discourse Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4190–4200, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (Lin et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1410.pdf
- Code
- additional community code