Abstract
Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem rely on annotated text segmentations (Elementary Discourse Units, EDUs) and sophisticated sparse or continuous features to extract syntactic information. In this paper we propose the first end-to-end discourse parser that jointly parses in both syntax and discourse levels, as well as the first syntacto-discourse treebank by integrating the Penn Treebank and the RST Treebank. Built upon our recent span-based constituency parser, this joint syntacto-discourse parser requires no preprocessing efforts such as segmentation or feature extraction, making discourse parsing more convenient. Empirically, our parser achieves the state-of-the-art end-to-end discourse parsing accuracy.- Anthology ID:
- D17-1225
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2117–2123
- Language:
- URL:
- https://aclanthology.org/D17-1225
- DOI:
- 10.18653/v1/D17-1225
- Cite (ACL):
- Kai Zhao and Liang Huang. 2017. Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2117–2123, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank (Zhao & Huang, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/D17-1225.pdf
- Code
- kaayy/josydipa
- Data
- Penn Treebank