Effectively training neural machine translation models with monolingual data

摘要

Improving neural machine translation models (NMT) with monolingual data has aroused more and more interests in this area and back-translation for monolingual data augmentation Sennrich et al. (2016) has been taken as a promising development recently. While the naive back-translation approach improves the translation performance substantially, we notice that its usage for monolingual data is not so effective because traditional NMT models make no distinction between the true parallel corpus and the back translated synthetic parallel corpus. This paper proposes a gate-enhanced NMT model which makes use of monolingual data more effectively. The central idea is to separate the data flow of monolingual data and parallel data into different channels by the elegant designed gate, which enables the model to perform different transformations according to the type of the input sequence, i.e., monolingual data and parallel data. Experiments on Chinese-English and English-German translation tasks show that our approach achieves substantial improvements over strong baselines and the gate-enhanced NMT model can utilize the source-side and target-side monolingual data at the same time. (C) 2018 Elsevier B.V. All rights reserved.