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.
Another approach for tackling the low-resource translation problem is multilingual neural machine translation @cite_14 , where different encoders and decoders for all languages with a shared attention mechanism are trained. This method tends to exploit the network architecture to relate low-resource pairs. Our method is different from it, which is more like a training method rather than network modification.
@cite_14
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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.