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        <title>Self-Adaptation for Unsupervised Domain Adaptation</title>
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        <namePart type="given">Xia</namePart>
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    <name type="personal">
        <namePart type="given">Danushka</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain. Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction. We then use the trained classifier to predict pseudo labels for the target domain unlabelled data. Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels. Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.</abstract>
    <identifier type="citekey">cui-bollegala-2019-self</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_025</identifier>
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        <url>https://aclanthology.org/R19-1025</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Self-Adaptation for Unsupervised Domain Adaptation
%A Cui, Xia
%A Bollegala, Danushka
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F cui-bollegala-2019-self
%X Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain. Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction. We then use the trained classifier to predict pseudo labels for the target domain unlabelled data. Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels. Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.
%R 10.26615/978-954-452-056-4_025
%U https://aclanthology.org/R19-1025
%U https://doi.org/10.26615/978-954-452-056-4_025
%P 213-222

Markdown (Informal)

[Self-Adaptation for Unsupervised Domain Adaptation](https://aclanthology.org/R19-1025) (Cui & Bollegala, RANLP 2019)

• Self-Adaptation for Unsupervised Domain Adaptation (Cui & Bollegala, RANLP 2019)

ACL

• Xia Cui and Danushka Bollegala. 2019. Self-Adaptation for Unsupervised Domain Adaptation. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 213–222, Varna, Bulgaria. INCOMA Ltd..