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Regularization of neural machine translation is still a significant problem,especially in low-resource settings. To mollify this problem, we proposeregressing word embeddings (ReWE) as a new regularization technique in a systemthat is jointly trained to predict the next word in the translation(categorical value) and its word embedding (continuous value). Such a jointtraining allows the proposed system to learn the distributional propertiesrepresented by the word embeddings, empirically improving the generalization tounseen sentences. Experiments over three translation datasets have showed aconsistent improvement over a strong baseline, ranging between 0.91 and 2.54BLEU points, and also a marked improvement over a state-of-the-art system.

ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems