Triangular Architecture for Rare Language Translation

Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma

Abstract: Neural Machine Translation (NMT) performs poor on the low-resource language pair (X,Z)$, especially when Z$ is a rare language. By introducing another rich language Y$, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data (Y,Z)$ (may be small) and (X,Y)$ (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, Z$ is taken as the intermediate latent variable, and translation models of Z$ are jointly optimized with a unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of (X,Y)$. 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.