Abstract
This paper demonstrates discopy, a novel framework that makes it easy to design components for end-to-end shallow discourse parsing. For the purpose of demonstration, we implement recent neural approaches and integrate contextualized word embeddings to predict explicit and non-explicit discourse relations. Our proposed neural feature-free system performs competitively to systems presented at the latest Shared Task on Shallow Discourse Parsing. Finally, a web front end is shown that simplifies the inspection of annotated documents. The source code, documentation, and pretrained models are publicly accessible.- Anthology ID:
- 2021.codi-main.12
- Volume:
- Proceedings of the 2nd Workshop on Computational Approaches to Discourse
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic and Online
- Venue:
- CODI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 128–133
- Language:
- URL:
- https://aclanthology.org/2021.codi-main.12
- DOI:
- 10.18653/v1/2021.codi-main.12
- Cite (ACL):
- René Knaebel. 2021. discopy: A Neural System for Shallow Discourse Parsing. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 128–133, Punta Cana, Dominican Republic and Online. Association for Computational Linguistics.
- Cite (Informal):
- discopy: A Neural System for Shallow Discourse Parsing (Knaebel, CODI 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.codi-main.12.pdf
- Code
- rknaebel/discopy