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• W2963679377 abstract "Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system." @default.
• W2963679377 created "2019-07-30" @default.
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• W2963679377 date "2019-01-01" @default.
• W2963679377 modified "2023-09-25" @default.
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• W2963679377 doi "https://doi.org/10.18653/v1/n19-1041" @default.
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