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Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation
[article]
2021
arXiv
pre-print
Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search -- the de facto standard inference algorithm in NMT -- and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this paper, we empirically investigate the properties of MBR
arXiv:2105.08504v1
fatcat:penictuu65fpdc7gkrq3asqjne