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arXiv · 2010.02352

Inference Strategies for Machine Translation with Conditional Masking

Julia Kreutzer·George Foster·Colin Cherry
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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 "mask-predict" algorithm, and provide analyses of its behavior on machine translation tasks.

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Julia Kreutzer, George Foster, Colin Cherry. 2020-10-05. Inference Strategies for Machine Translation with Conditional Masking. https://arxiv.org/abs/2010.02352

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