Abstract
In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.- Anthology ID:
- P19-1562
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5592–5598
- Language:
- URL:
- https://aclanthology.org/P19-1562
- DOI:
- 10.18653/v1/P19-1562
- Cite (ACL):
- Zhisong Zhang, Xuezhe Ma, and Eduard Hovy. 2019. An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5592–5598, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing (Zhang et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1562.pdf
- Code
- zzsfornlp/zmsp