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While synthetic bilingual corpora have demonstrated their effectiveness inlow-resource neural machine translation (NMT), adding more synthetic data oftendeteriorates translation performance. In this work, we propose alternatedtraining with synthetic and authentic data for NMT. The basic idea is toalternate synthetic and authentic corpora iteratively during training. Comparedwith previous work, we introduce authentic data as guidance to prevent thetraining of NMT models from being disturbed by noisy synthetic data.Experiments on Chinese-English and German-English translation tasks show thatour approach improves the performance over several strong baselines. Wevisualize the BLEU landscape to further investigate the role of authentic andsynthetic data during alternated training. From the visualization, we find thatauthentic data helps to direct the NMT model parameters towards points withhigher BLEU scores and leads to consistent translation performance improvement.

Alternated Training with Synthetic and Authentic Data for Neural Machine Translation