Video Summary

The paper proposes a new method for training neural machine translation models. The method is based on the idea of self-supervised learning, where the model is trained to predict its own output. This is done by masking some of the tokens in the input sequence and then having the model predict the masked tokens. The authors show that this method can improve the performance of neural machine translation models on a variety of tasks.