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    <titleInfo>
        <title>Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation</title>
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    <name type="personal">
        <namePart type="given">Francine</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Yan-Ying</namePart>
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        <dateIssued>2019-07</dateIssued>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</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">Traum</namePart>
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            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
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    <abstract>A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.</abstract>
    <identifier type="citekey">chen-chen-2019-adversarial</identifier>
    <identifier type="doi">10.18653/v1/P19-1211</identifier>
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        <url>https://aclanthology.org/P19-1211</url>
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    <part>
        <date>2019-07</date>
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%0 Conference Proceedings
%T Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation
%A Chen, Francine
%A Chen, Yan-Ying
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-chen-2019-adversarial
%X A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.
%R 10.18653/v1/P19-1211
%U https://aclanthology.org/P19-1211
%U https://doi.org/10.18653/v1/P19-1211
%P 2197-2203

Markdown (Informal)

[Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation](https://aclanthology.org/P19-1211) (Chen & Chen, ACL 2019)

• Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation (Chen & Chen, ACL 2019)

ACL

• Francine Chen and Yan-Ying Chen. 2019. Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2197–2203, Florence, Italy. Association for Computational Linguistics.