On Using Monolingual Corpora in Neural Machine Translation

Understand

Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation.

• Arguably, one of the major factors behind this success has been the availability of high quality parallel corpora.
• In this work, we investigate how to leverage abundant monolingual corpora for neural machine translation.
• Compared to a phrase-based and hierarchical baseline, we obtain up to $1.96$ BLEU improvement on the low-resource language pair Turkish-English, and $1.59$ BLEU on the focused domain task of Chinese-English chat messages.