%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