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• W3212880513 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 generation. 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." @default.
• W3212880513 created "2021-11-22" @default.
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• W3212880513 date "2021-10-25" @default.
• W3212880513 modified "2023-09-27" @default.
• W3212880513 title "Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs." @default.
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• W3212880513 hasPublicationYear "2021" @default.
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