Abstract
We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.- Anthology ID:
- N18-1013
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 141–151
- Language:
- URL:
- https://aclanthology.org/N18-1013
- DOI:
- 10.18653/v1/N18-1013
- Cite (ACL):
- Zeyu Dai and Ruihong Huang. 2018. Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 141–151, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (Dai & Huang, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N18-1013.pdf