A Span Selection Model for Semantic Role Labeling

Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto


Abstract
We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.
Anthology ID:
D18-1191
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1630–1642
Language:
URL:
https://aclanthology.org/D18-1191
DOI:
10.18653/v1/D18-1191
Bibkey:
Cite (ACL):
Hiroki Ouchi, Hiroyuki Shindo, and Yuji Matsumoto. 2018. A Span Selection Model for Semantic Role Labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1630–1642, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A Span Selection Model for Semantic Role Labeling (Ouchi et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1191.pdf
Code
 hiroki13/span-based-srl +  additional community code
Data
OntoNotes 5.0