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        <title>A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
%A Marcheggiani, Diego
%A Frolov, Anton
%A Titov, Ivan
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F marcheggiani-etal-2017-simple
%X 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.
%R 10.18653/v1/K17-1041
%U https://aclanthology.org/K17-1041
%U https://doi.org/10.18653/v1/K17-1041
%P 411-420

Markdown (Informal)

[A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling](https://aclanthology.org/K17-1041) (Marcheggiani et al., CoNLL 2017)

• A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling (Marcheggiani et al., CoNLL 2017)

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.