A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling

Diego Marcheggiani, Anton Frolov, Ivan Titov


Abstract
We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.
Anthology ID:
K17-1041
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
411–420
Language:
URL:
https://aclanthology.org/K17-1041
DOI:
10.18653/v1/K17-1041
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/K17-1041.pdf
Code
 diegma/neural-dep-srl +  additional community code