Abstract
We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.- Anthology ID:
- 2020.acl-main.299
- 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:
- 3267–3277
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.299
- DOI:
- 10.18653/v1/2020.acl-main.299
- Cite (ACL):
- Yang Wei, Yuanbin Wu, and Man Lan. 2020. A Span-based Linearization for Constituent Trees. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3267–3277, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Span-based Linearization for Constituent Trees (Wei et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2020.acl-main.299.pdf
- Code
- AntNLP/span-linearization-parser
- Data
- Penn Treebank