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        <title>A Unified Architecture for Semantic Role Labeling and Relation Classification</title>
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        <namePart type="given">Wanxiang</namePart>
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    <name type="personal">
        <namePart type="given">Haifeng</namePart>
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    <name type="personal">
        <namePart type="given">Ting</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Jun</namePart>
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    <abstract>This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly studied separately in prior work, we show that the two tasks can be effectively connected and modeled using a general architecture. Experiments on CoNLL-2009 benchmark datasets show that our SRL models significantly outperform state-of-the-art approaches. Our RC models also yield competitive performance with the best published records. Furthermore, we show that the two tasks can be trained jointly with multi-task learning, resulting in additive significant improvements for SRL.</abstract>
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        <url>https://aclanthology.org/C16-1120</url>
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%0 Conference Proceedings
%T A Unified Architecture for Semantic Role Labeling and Relation Classification
%A Guo, Jiang
%A Che, Wanxiang
%A Wang, Haifeng
%A Liu, Ting
%A Xu, Jun
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F guo-etal-2016-unified
%X This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly studied separately in prior work, we show that the two tasks can be effectively connected and modeled using a general architecture. Experiments on CoNLL-2009 benchmark datasets show that our SRL models significantly outperform state-of-the-art approaches. Our RC models also yield competitive performance with the best published records. Furthermore, we show that the two tasks can be trained jointly with multi-task learning, resulting in additive significant improvements for SRL.
%U https://aclanthology.org/C16-1120
%P 1264-1274

Markdown (Informal)

[A Unified Architecture for Semantic Role Labeling and Relation Classification](https://aclanthology.org/C16-1120) (Guo et al., COLING 2016)

• A Unified Architecture for Semantic Role Labeling and Relation Classification (Guo et al., COLING 2016)

ACL

• Jiang Guo, Wanxiang Che, Haifeng Wang, Ting Liu, and Jun Xu. 2016. A Unified Architecture for Semantic Role Labeling and Relation Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1264–1274, Osaka, Japan. The COLING 2016 Organizing Committee.