Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages

Understand

The advent of deep learning has led to a significant gain in machine translation.

• However, most of the studies required a large parallel dataset which is scarce and expensive to construct and even unavailable for some languages.
• This paper presents a simple yet effective method to tackle this problem for low-resource languages by augmenting high-quality sentence pairs and training NMT models in a semi-supervised manner.
• Specifically, our approach combines the cross-entropy loss for supervised learning with KL Divergence for unsupervised fashion given pseudo and augmented target sentences derived from the model.