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        <title>Paraphrases as Foreign Languages in Multilingual Neural Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Zhong</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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            <namePart type="given">Fernando</namePart>
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    <abstract>Paraphrases, rewordings of the same semantic meaning, are useful for improving generalization and translation. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones, while adding both lifts performance further. We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source single-target NMT baseline.</abstract>
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%0 Conference Proceedings
%T Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
%A Zhou, Zhong
%A Sperber, Matthias
%A Waibel, Alexander
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhou-etal-2019-paraphrases
%X Paraphrases, rewordings of the same semantic meaning, are useful for improving generalization and translation. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones, while adding both lifts performance further. We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source single-target NMT baseline.
%R 10.18653/v1/P19-2015
%U https://aclanthology.org/P19-2015
%U https://doi.org/10.18653/v1/P19-2015
%P 113-122

Markdown (Informal)

[Paraphrases as Foreign Languages in Multilingual Neural Machine Translation](https://aclanthology.org/P19-2015) (Zhou et al., ACL 2019)

• Paraphrases as Foreign Languages in Multilingual Neural Machine Translation (Zhou et al., ACL 2019)

ACL

• Zhong Zhou, Matthias Sperber, and Alexander Waibel. 2019. Paraphrases as Foreign Languages in Multilingual Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 113–122, Florence, Italy. Association for Computational Linguistics.