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

NAACL 2019 Inigo Jauregi UnanueEhsan Zare BorzeshiNazanin EsmailiMassimo Piccardi

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)... (read more)

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