Abstract
Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call DENSE (as shorthand for Dependency Neural Selection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, DeNSe generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate DeNSe on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.- Anthology ID:
- E17-1063
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 665–676
- Language:
- URL:
- https://aclanthology.org/E17-1063
- DOI:
- Cite (ACL):
- Xingxing Zhang, Jianpeng Cheng, and Mirella Lapata. 2017. Dependency Parsing as Head Selection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 665–676, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Dependency Parsing as Head Selection (Zhang et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/landing_page/E17-1063.pdf
- Code
- XingxingZhang/dense_parser