@inproceedings{kreutzer-etal-2020-inference,
    title = "Inference Strategies for Machine Translation with Conditional Masking",
    author = "Kreutzer, Julia  and
      Foster, George  and
      Cherry, Colin",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.emnlp-main.465/",
    doi = "10.18653/v1/2020.emnlp-main.465",
    pages = "5774--5782",
    abstract = "Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation. Given a trained CMLM, however, it is not clear what the best inference strategy is. We formulate masked inference as a factorization of conditional probabilities of partial sequences, show that this does not harm performance, and investigate a number of simple heuristics motivated by this perspective. We identify a thresholding strategy that has advantages over the standard {\textquotedblleft}mask-predict{\textquotedblright} algorithm, and provide analyses of its behavior on machine translation tasks."
}