This work introduces conditional masked language models and a novel mask-predict decoding algorithm that leverages their parallelism to generate text in a constant number of decoding iterations.
mask-predict repeatedly masks out and repredicts the subset of words in the current translation that the model is least confident about, in contrast to recent parallel decoding translation approaches that repeatedly predict the entire sequence .