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        <title>High-order Semantic Role Labeling</title>
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    <name type="personal">
        <namePart type="given">Zuchao</namePart>
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    <name type="personal">
        <namePart type="given">Kevin</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time. High-order features and structure learning were very common in modeling such correlations before the neural network era. In this paper, we introduce a high-order graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new state-of-the-art results.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.102</identifier>
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        <url>https://aclanthology.org/2020.findings-emnlp.102</url>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T High-order Semantic Role Labeling
%A Li, Zuchao
%A Zhao, Hai
%A Wang, Rui
%A Parnow, Kevin
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-high
%X Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time. High-order features and structure learning were very common in modeling such correlations before the neural network era. In this paper, we introduce a high-order graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new state-of-the-art results.
%R 10.18653/v1/2020.findings-emnlp.102
%U https://aclanthology.org/2020.findings-emnlp.102
%U https://doi.org/10.18653/v1/2020.findings-emnlp.102
%P 1134-1151

Markdown (Informal)

[High-order Semantic Role Labeling](https://aclanthology.org/2020.findings-emnlp.102) (Li et al., Findings 2020)

• High-order Semantic Role Labeling (Li et al., Findings 2020)

ACL

• Zuchao Li, Hai Zhao, Rui Wang, and Kevin Parnow. 2020. High-order Semantic Role Labeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1134–1151, Online. Association for Computational Linguistics.