  Compared with only using limited authentic parallel data as training corpus,
many studies have proved that incorporating synthetic parallel data, which
generated by back translation (BT) or forward translation (FT, or
selftraining), into the NMT training process can significantly improve
translation quality. However, as a well-known shortcoming, synthetic parallel
data is noisy because they are generated by an imperfect NMT system. As a
result, the improvements in translation quality bring by the synthetic parallel
data are greatly diminished. In this paper, we propose a novel Auto- Repair
(AR) framework to improve the quality of synthetic data. Our proposed AR model
can learn the transformation from low quality (noisy) input sentence to high
quality sentence based on large scale monolingual data with BT and FT
techniques. The noise in synthetic parallel data will be sufficiently
eliminated by the proposed AR model and then the repaired synthetic parallel
data can help the NMT models to achieve larger improvements. Experimental
results show that our approach can effective improve the quality of synthetic
parallel data and the NMT model with the repaired synthetic data achieves
consistent improvements on both WMT14 EN!DE and IWSLT14 DE!EN translation
tasks.
