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        <title>Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network</title>
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        <namePart type="given">Lisheng</namePart>
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        <namePart type="given">Thien</namePart>
        <namePart type="given">Huu</namePart>
        <namePart type="family">Nguyen</namePart>
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    <name type="personal">
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        <namePart type="family">Min</namePart>
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    <name type="personal">
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        <dateIssued>2017-nov</dateIssued>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <publisher>Asian Federation of Natural Language Processing</publisher>
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    <abstract>Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor’s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domain-specific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.</abstract>
    <identifier type="citekey">fu-etal-2017-domain</identifier>
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        <url>https://aclanthology.org/I17-2072</url>
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        <date>2017-nov</date>
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            <start>425</start>
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%0 Conference Proceedings
%T Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
%A Fu, Lisheng
%A Nguyen, Thien Huu
%A Min, Bonan
%A Grishman, Ralph
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 nov
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F fu-etal-2017-domain
%X Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor’s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domain-specific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.
%U https://aclanthology.org/I17-2072
%P 425-429

Markdown (Informal)

[Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network](https://aclanthology.org/I17-2072) (Fu et al., IJCNLP 2017)

• Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network (Fu et al., IJCNLP 2017)

ACL

• Lisheng Fu, Thien Huu Nguyen, Bonan Min, and Ralph Grishman. 2017. Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 425–429, Taipei, Taiwan. Asian Federation of Natural Language Processing.