Neural Machine Translation (NMT) performs poor on the low-resource language pair @math , especially when @math is a rare language. By introducing another rich language @math , we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data @math (may be small) and @math (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, @math is taken as the intermediate latent variable, and translation models of @math are jointly optimized with a unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of @math . Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.
We propose multi-way, multilingual neural machine translation. The proposed approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only linearly with the number of languages. This is made possible by having a single attention mechanism that is shared across all language pairs. We train the proposed multi-way, multilingual model on ten language pairs from WMT'15 simultaneously and observe clear performance improvements over models trained on only one language pair. In particular, we observe that the proposed model significantly improves the translation quality of low-resource language pairs.