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        <title>Multilingual Whispers: Generating Paraphrases with Translation</title>
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        <namePart type="given">Christian</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>Naturally occurring paraphrase data, such as multiple news stories about the same event, is a useful but rare resource. This paper compares translation-based paraphrase gathering using human, automatic, or hybrid techniques to monolingual paraphrasing by experts and non-experts. We gather translations, paraphrases, and empirical human quality assessments of these approaches. Neural machine translation techniques, especially when pivoting through related languages, provide a relatively robust source of paraphrases with diversity comparable to expert human paraphrases. Surprisingly, human translators do not reliably outperform neural systems. The resulting data release will not only be a useful test set, but will also allow additional explorations in translation and paraphrase quality assessments and relationships.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5503</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Multilingual Whispers: Generating Paraphrases with Translation
%A Federmann, Christian
%A Elachqar, Oussama
%A Quirk, Chris
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F federmann-etal-2019-multilingual
%X Naturally occurring paraphrase data, such as multiple news stories about the same event, is a useful but rare resource. This paper compares translation-based paraphrase gathering using human, automatic, or hybrid techniques to monolingual paraphrasing by experts and non-experts. We gather translations, paraphrases, and empirical human quality assessments of these approaches. Neural machine translation techniques, especially when pivoting through related languages, provide a relatively robust source of paraphrases with diversity comparable to expert human paraphrases. Surprisingly, human translators do not reliably outperform neural systems. The resulting data release will not only be a useful test set, but will also allow additional explorations in translation and paraphrase quality assessments and relationships.
%R 10.18653/v1/D19-5503
%U https://aclanthology.org/D19-5503
%U https://doi.org/10.18653/v1/D19-5503
%P 17-26

Markdown (Informal)

[Multilingual Whispers: Generating Paraphrases with Translation](https://aclanthology.org/D19-5503) (Federmann et al., WNUT 2019)

• Multilingual Whispers: Generating Paraphrases with Translation (Federmann et al., WNUT 2019)

ACL

• Christian Federmann, Oussama Elachqar, and Chris Quirk. 2019. Multilingual Whispers: Generating Paraphrases with Translation. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 17–26, Hong Kong, China. Association for Computational Linguistics.