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    <titleInfo>
        <title>Adversarial training for multi-context joint entity and relation extraction</title>
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    <name type="personal">
        <namePart type="given">Giannis</namePart>
        <namePart type="family">Bekoulis</namePart>
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    <name type="personal">
        <namePart type="given">Johannes</namePart>
        <namePart type="family">Deleu</namePart>
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        <namePart type="given">Thomas</namePart>
        <namePart type="family">Demeester</namePart>
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    <name type="personal">
        <namePart type="given">Chris</namePart>
        <namePart type="family">Develder</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).</abstract>
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    <identifier type="doi">10.18653/v1/D18-1307</identifier>
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        <url>https://aclanthology.org/D18-1307</url>
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        <date>2018-oct-nov</date>
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            <start>2830</start>
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%0 Conference Proceedings
%T Adversarial training for multi-context joint entity and relation extraction
%A Bekoulis, Giannis
%A Deleu, Johannes
%A Demeester, Thomas
%A Develder, Chris
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F bekoulis-etal-2018-adversarial
%X Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).
%R 10.18653/v1/D18-1307
%U https://aclanthology.org/D18-1307
%U https://doi.org/10.18653/v1/D18-1307
%P 2830-2836

Markdown (Informal)

[Adversarial training for multi-context joint entity and relation extraction](https://aclanthology.org/D18-1307) (Bekoulis et al., EMNLP 2018)

• Adversarial training for multi-context joint entity and relation extraction (Bekoulis et al., EMNLP 2018)

ACL

• Giannis Bekoulis, Johannes Deleu, Thomas Demeester, and Chris Develder. 2018. Adversarial training for multi-context joint entity and relation extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2830–2836, Brussels, Belgium. Association for Computational Linguistics.