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    <titleInfo>
        <title>Syntax Matters! Syntax-Controlled in Text Style Transfer</title>
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        <namePart type="given">Zhiqiang</namePart>
        <namePart type="family">Hu</namePart>
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    <name type="personal">
        <namePart type="given">Charu</namePart>
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        <namePart type="family">Aggarwal</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="family">Mitkov</namePart>
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    <abstract>Existing text style transfer (TST) methods rely on style classifiers to disentangle the text’s content and style attributes for text style transfer. While the style classifier plays a critical role in existing TST methods, there is no known investigation on its effect on the TST methods. In this paper, we conduct an empirical study on the limitations of the style classifiers used in existing TST methods. We demonstrated that the existing style classifiers cannot learn sentence syntax effectively and ultimately worsen existing TST models’ performance. To address this issue, we propose a novel Syntax-Aware Controllable Generation (SACG) model, which includes a syntax-aware style classifier that ensures learned style latent representations effectively capture the sentence structure for TST. Through extensive experiments on two popular text style transfer tasks, we show that our proposed method significantly outperforms twelve state-of-the-art methods. Our case studies have also demonstrated SACG’s ability to generate fluent target-style sentences that preserved the original content.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.64</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Syntax Matters! Syntax-Controlled in Text Style Transfer
%A Hu, Zhiqiang
%A Lee, Roy Ka-Wei
%A Aggarwal, Charu C.
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F hu-etal-2021-syntax
%X Existing text style transfer (TST) methods rely on style classifiers to disentangle the text’s content and style attributes for text style transfer. While the style classifier plays a critical role in existing TST methods, there is no known investigation on its effect on the TST methods. In this paper, we conduct an empirical study on the limitations of the style classifiers used in existing TST methods. We demonstrated that the existing style classifiers cannot learn sentence syntax effectively and ultimately worsen existing TST models’ performance. To address this issue, we propose a novel Syntax-Aware Controllable Generation (SACG) model, which includes a syntax-aware style classifier that ensures learned style latent representations effectively capture the sentence structure for TST. Through extensive experiments on two popular text style transfer tasks, we show that our proposed method significantly outperforms twelve state-of-the-art methods. Our case studies have also demonstrated SACG’s ability to generate fluent target-style sentences that preserved the original content.
%U https://aclanthology.org/2021.ranlp-1.64
%P 566-575

Markdown (Informal)

[Syntax Matters! Syntax-Controlled in Text Style Transfer](https://aclanthology.org/2021.ranlp-1.64) (Hu et al., RANLP 2021)

• Syntax Matters! Syntax-Controlled in Text Style Transfer (Hu et al., RANLP 2021)

ACL

• Zhiqiang Hu, Roy Ka-Wei Lee, and Charu C. Aggarwal. 2021. Syntax Matters! Syntax-Controlled in Text Style Transfer. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 566–575, Held Online. INCOMA Ltd..