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        <title>Unsupervised Text Style Transfer with Padded Masked Language Models</title>
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        <namePart type="given">Eric</namePart>
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        <namePart type="given">Aliaksei</namePart>
        <namePart type="family">Severyn</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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            <namePart type="given">Trevor</namePart>
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            <namePart type="family">He</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases when no parallel source–target pairs are available, we train masked language models (MLMs) for both the source and the target domain. Then we find the text spans where the two models disagree the most in terms of likelihood. This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain. The deleted tokens are replaced with the target MLM, and by using a padded MLM variant, we avoid having to predetermine the number of inserted tokens. Our experiments on sentence fusion and sentiment transfer demonstrate that Masker performs competitively in a fully unsupervised setting. Moreover, in low-resource settings, it improves supervised methods’ accuracy by over 10 percentage points when pre-training them on silver training data generated by Masker.</abstract>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Unsupervised Text Style Transfer with Padded Masked Language Models
%A Malmi, Eric
%A Severyn, Aliaksei
%A Rothe, Sascha
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F malmi-etal-2020-unsupervised
%X We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases when no parallel source–target pairs are available, we train masked language models (MLMs) for both the source and the target domain. Then we find the text spans where the two models disagree the most in terms of likelihood. This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain. The deleted tokens are replaced with the target MLM, and by using a padded MLM variant, we avoid having to predetermine the number of inserted tokens. Our experiments on sentence fusion and sentiment transfer demonstrate that Masker performs competitively in a fully unsupervised setting. Moreover, in low-resource settings, it improves supervised methods’ accuracy by over 10 percentage points when pre-training them on silver training data generated by Masker.
%R 10.18653/v1/2020.emnlp-main.699
%U https://aclanthology.org/2020.emnlp-main.699/
%U https://doi.org/10.18653/v1/2020.emnlp-main.699
%P 8671-8680

Markdown (Informal)

[Unsupervised Text Style Transfer with Padded Masked Language Models](https://aclanthology.org/2020.emnlp-main.699/) (Malmi et al., EMNLP 2020)

• Unsupervised Text Style Transfer with Padded Masked Language Models (Malmi et al., EMNLP 2020)

ACL

• Eric Malmi, Aliaksei Severyn, and Sascha Rothe. 2020. Unsupervised Text Style Transfer with Padded Masked Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8671–8680, Online. Association for Computational Linguistics.