Abstract
Recent advances in RST discourse parsing have focused on two modeling paradigms: (a) high order parsers which jointly predict the tree structure of the discourse and the relations it encodes; or (b) linear-time parsers which are efficient but mostly based on local features. In this work, we propose a linear-time parser with a novel way of representing discourse constituents based on neural networks which takes into account global contextual information and is able to capture long-distance dependencies. Experimental results show that our parser obtains state-of-the art performance on benchmark datasets, while being efficient (with time complexity linear in the number of sentences in the document) and requiring minimal feature engineering.- Anthology ID:
- D17-1133
- 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:
- 1289–1298
- Language:
- URL:
- https://aclanthology.org/D17-1133
- DOI:
- 10.18653/v1/D17-1133
- Cite (ACL):
- Yang Liu and Mirella Lapata. 2017. Learning Contextually Informed Representations for Linear-Time Discourse Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1289–1298, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Learning Contextually Informed Representations for Linear-Time Discourse Parsing (Liu & Lapata, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D17-1133.pdf