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In neural machine translation, a source sequence of words is encoded into avector from which a target sequence is generated in the decoding phase.Differently from statistical machine translation, the associations betweensource words and their possible target counterparts are not explicitly stored.Source and target words are at the two ends of a long information processingprocedure, mediated by hidden states at both the source encoding and the targetdecoding phases. This makes it possible that a source word is incorrectlytranslated into a target word that is not any of its admissible equivalentcounterparts in the target language.  In this paper, we seek to somewhat shorten the distance between source andtarget words in that procedure, and thus strengthen their association, by meansof a method we term bridging source and target word embeddings. We experimentwith three strategies: (1) a source-side bridging model, where source wordembeddings are moved one step closer to the output target sequence; (2) atarget-side bridging model, which explores the more relevant source wordembeddings for the prediction of the target sequence; and (3) a direct bridgingmodel, which directly connects source and target word embeddings seeking tominimize errors in the translation of ones by the others.  Experiments and analysis presented in this paper demonstrate that theproposed bridging models are able to significantly improve quality of bothsentence translation, in general, and alignment and translation of individualsource words with target words, in particular.

Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings