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• W3175095087 abstract "UNMT tackles translation on monolingual corpora in two required languages. Since there is no explicitly cross-lingual signal, pre-training and synthetic sentence pairs are significant to the success of UNMT. In this work, we empirically study the core training procedure of UNMT to analyze the synthetic sentence pairs obtained from back-translation. We introduce new losses to UNMT to regularize the synthetic sentence pairs by jointly training the UNMT objective and the regularization objective. Our comprehensive experiments support that our method can generally improve the performance of currently successful models on three similar pairs {French, German, Romanian} English and one dissimilar pair Russian English with acceptably additional cost." @default.
• W3175095087 created "2021-07-05" @default.
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• W3175095087 date "2021-05-18" @default.
• W3175095087 modified "2023-10-16" @default.
• W3175095087 title "Empirical Regularization for Synthetic Sentence Pairs in Unsupervised Neural Machine Translation" @default.
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• W3175095087 doi "https://doi.org/10.1609/aaai.v35i14.17479" @default.
• W3175095087 hasPublicationYear "2021" @default.
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