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        <title>Disentangled Representation Learning for Non-Parallel Text Style Transfer</title>
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        <namePart type="given">Vineet</namePart>
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    <name type="personal">
        <namePart type="given">Hareesh</namePart>
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    <name type="personal">
        <namePart type="given">Olga</namePart>
        <namePart type="family">Vechtomova</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
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    <abstract>This paper tackles the problem of disentangling the latent representations of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for style prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning can be applied to style transfer on non-parallel corpora. We achieve high performance in terms of transfer accuracy, content preservation, and language fluency, in comparison to various previous approaches.</abstract>
    <identifier type="citekey">john-etal-2019-disentangled</identifier>
    <identifier type="doi">10.18653/v1/P19-1041</identifier>
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%0 Conference Proceedings
%T Disentangled Representation Learning for Non-Parallel Text Style Transfer
%A John, Vineet
%A Mou, Lili
%A Bahuleyan, Hareesh
%A Vechtomova, Olga
%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 john-etal-2019-disentangled
%X This paper tackles the problem of disentangling the latent representations of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for style prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning can be applied to style transfer on non-parallel corpora. We achieve high performance in terms of transfer accuracy, content preservation, and language fluency, in comparison to various previous approaches.
%R 10.18653/v1/P19-1041
%U https://aclanthology.org/P19-1041
%U https://doi.org/10.18653/v1/P19-1041
%P 424-434

Markdown (Informal)

[Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://aclanthology.org/P19-1041) (John et al., ACL 2019)

• Disentangled Representation Learning for Non-Parallel Text Style Transfer (John et al., ACL 2019)

ACL

• Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. 2019. Disentangled Representation Learning for Non-Parallel Text Style Transfer. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 424–434, Florence, Italy. Association for Computational Linguistics.