Abstract
We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.- Anthology ID:
- P19-1629
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6248–6262
- Language:
- URL:
- https://aclanthology.org/P19-1629
- DOI:
- 10.18653/v1/P19-1629
- Cite (ACL):
- Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2019. Discourse Representation Parsing for Sentences and Documents. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6248–6262, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Discourse Representation Parsing for Sentences and Documents (Liu et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P19-1629.pdf