Abstract
We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.- Anthology ID:
- Q15-1003
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 3
- Month:
- Year:
- 2015
- Address:
- Cambridge, MA
- Editors:
- Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 29–41
- Language:
- URL:
- https://aclanthology.org/Q15-1003
- DOI:
- 10.1162/tacl_a_00120
- Cite (ACL):
- Oscar Täckström, Kuzman Ganchev, and Dipanjan Das. 2015. Efficient Inference and Structured Learning for Semantic Role Labeling. Transactions of the Association for Computational Linguistics, 3:29–41.
- Cite (Informal):
- Efficient Inference and Structured Learning for Semantic Role Labeling (Täckström et al., TACL 2015)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q15-1003.pdf
- Data
- FrameNet