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
- Cite (ACL):
- Diego Marcheggiani, Anton Frolov, and Ivan Titov. 2017. A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 411–420, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling (Marcheggiani et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/K17-1041.pdf
- Code
- diegma/neural-dep-srl + additional community code