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    <titleInfo>
        <title>Aspect-augmented Adversarial Networks for Domain Adaptation</title>
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    <name type="personal">
        <namePart type="given">Yuan</namePart>
        <namePart type="family">Zhang</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Regina</namePart>
        <namePart type="family">Barzilay</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Tommi</namePart>
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    <abstract>We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain. Rather than training on target labels, we use a few keywords pertaining to source and target aspects indicating sentence relevance instead of document class labels. Documents are encoded by learning to embed and softly select relevant sentences in an aspect-dependent manner. A shared classifier is trained on the source encoded documents and labels, and applied to target encoded documents. We ensure transfer through aspect-adversarial training so that encoded documents are, as sets, aspect-invariant. Experimental results demonstrate that our approach outperforms different baselines and model variants on two datasets, yielding an improvement of 27% on a pathology dataset and 5% on a review dataset.</abstract>
    <identifier type="citekey">zhang-etal-2017-aspect</identifier>
    <identifier type="doi">10.1162/tacl_a_00077</identifier>
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        <url>https://aclanthology.org/Q17-1036</url>
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        <date>2017</date>
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            <start>515</start>
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%0 Journal Article
%T Aspect-augmented Adversarial Networks for Domain Adaptation
%A Zhang, Yuan
%A Barzilay, Regina
%A Jaakkola, Tommi
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F zhang-etal-2017-aspect
%X We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain. Rather than training on target labels, we use a few keywords pertaining to source and target aspects indicating sentence relevance instead of document class labels. Documents are encoded by learning to embed and softly select relevant sentences in an aspect-dependent manner. A shared classifier is trained on the source encoded documents and labels, and applied to target encoded documents. We ensure transfer through aspect-adversarial training so that encoded documents are, as sets, aspect-invariant. Experimental results demonstrate that our approach outperforms different baselines and model variants on two datasets, yielding an improvement of 27% on a pathology dataset and 5% on a review dataset.
%R 10.1162/tacl_a_00077
%U https://aclanthology.org/Q17-1036
%U https://doi.org/10.1162/tacl_a_00077
%P 515-528

Markdown (Informal)

[Aspect-augmented Adversarial Networks for Domain Adaptation](https://aclanthology.org/Q17-1036) (Zhang et al., TACL 2017)

• Aspect-augmented Adversarial Networks for Domain Adaptation (Zhang et al., TACL 2017)

ACL

• Yuan Zhang, Regina Barzilay, and Tommi Jaakkola. 2017. Aspect-augmented Adversarial Networks for Domain Adaptation. Transactions of the Association for Computational Linguistics, 5:515–528.