Abstract
In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the input. We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).- Anthology ID:
- P17-1076
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 818–827
- Language:
- URL:
- https://aclanthology.org/P17-1076
- DOI:
- 10.18653/v1/P17-1076
- Cite (ACL):
- Mitchell Stern, Jacob Andreas, and Dan Klein. 2017. A Minimal Span-Based Neural Constituency Parser. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 818–827, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Minimal Span-Based Neural Constituency Parser (Stern et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1076.pdf
- Data
- Penn Treebank