Abstract
We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word’s tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.- Anthology ID:
- 2020.acl-main.557
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6255–6261
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.557
- DOI:
- 10.18653/v1/2020.acl-main.557
- Cite (ACL):
- Nikita Kitaev and Dan Klein. 2020. Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6255–6261, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference (Kitaev & Klein, ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.acl-main.557.pdf
- Code
- nikitakit/tetra-tagging + additional community code
- Data
- Penn Treebank