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    <abstract>We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into “meaning spaces” – replacing the final softmax layer with word embeddings. This architectural modification, plus a training procedure that incorporates an autoencoding objective, enables effective parameter sharing across languages for more fluent monolingual rewriting, and facilitates fluency and diversity in the generated outputs. Our continuous-output paraphrase generation models outperform zero-shot paraphrasing baselines when evaluated on two languages using a battery of computational metrics as well as in human assessment.</abstract>
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%0 Conference Proceedings
%T Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
%A Jegadeesan, Monisha
%A Kumar, Sachin
%A Wieting, John
%A Tsvetkov, Yulia
%Y Ataman, Duygu
%Y Birch, Alexandra
%Y Conneau, Alexis
%Y Firat, Orhan
%Y Ruder, Sebastian
%Y Sahin, Gozde Gul
%S Proceedings of the 1st Workshop on Multilingual Representation Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F jegadeesan-etal-2021-improving
%X We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into “meaning spaces” – replacing the final softmax layer with word embeddings. This architectural modification, plus a training procedure that incorporates an autoencoding objective, enables effective parameter sharing across languages for more fluent monolingual rewriting, and facilitates fluency and diversity in the generated outputs. Our continuous-output paraphrase generation models outperform zero-shot paraphrasing baselines when evaluated on two languages using a battery of computational metrics as well as in human assessment.
%R 10.18653/v1/2021.mrl-1.15
%U https://aclanthology.org/2021.mrl-1.15
%U https://doi.org/10.18653/v1/2021.mrl-1.15
%P 166-175

Markdown (Informal)

[Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs](https://aclanthology.org/2021.mrl-1.15) (Jegadeesan et al., MRL 2021)

• Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs (Jegadeesan et al., MRL 2021)

ACL

• Monisha Jegadeesan, Sachin Kumar, John Wieting, and Yulia Tsvetkov. 2021. Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 166–175, Punta Cana, Dominican Republic. Association for Computational Linguistics.