“Mask-Predict: Parallel Decoding of Conditional Masked Language Models”, Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer2019-04-19 (knowledge distillation, Transformer)⁠:

Most machine translation systems generate text autoregressively from left to right.

We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about.

By applying this strategy for a constant number of iterations [and using knowledge distillation], our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding faster.