Abstract
We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points. We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing. Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing.- Anthology ID:
- 2021.eacl-main.60
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 715–726
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.60
- DOI:
- 10.18653/v1/2021.eacl-main.60
- Cite (ACL):
- Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2021. Top-down Discourse Parsing via Sequence Labelling. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 715–726, Online. Association for Computational Linguistics.
- Cite (Informal):
- Top-down Discourse Parsing via Sequence Labelling (Koto et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2021.eacl-main.60.pdf
- Code
- fajri91/NeuralRST-TopDown
- Data
- RST-DT