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Neural machine translation, a recently proposed approach to machinetranslation based purely on neural networks, has shown promising resultscompared to the existing approaches such as phrase-based statistical machinetranslation. Despite its recent success, neural machine translation has itslimitation in handling a larger vocabulary, as training complexity as well asdecoding complexity increase proportionally to the number of target words. Inthis paper, we propose a method that allows us to use a very large targetvocabulary without increasing training complexity, based on importancesampling. We show that decoding can be efficiently done even with the modelhaving a very large target vocabulary by selecting only a small subset of thewhole target vocabulary. The models trained by the proposed approach areempirically found to outperform the baseline models with a small vocabulary aswell as the LSTM-based neural machine translation models. Furthermore, when weuse the ensemble of a few models with very large target vocabularies, weachieve the state-of-the-art translation performance (measured by BLEU) on theEnglish->German translation and almost as high performance as state-of-the-artEnglish->French translation system.

On Using Very Large Target Vocabulary for Neural Machine Translation