In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation (NAT).
we first empirically study the functionalities of the encoder and the decoder in NAT models, and find that the encoder takes a more important role than the decoder regarding the translation quality.