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In Neural Machine Translation, it is typically assumed that the sentence withthe highest estimated probability should also be the translation with thehighest quality as measured by humans. In this work, we question thisassumption and show that model estimates and translation quality only vaguelycorrelate. We apply Minimum Bayes Risk (MBR) decoding on unbiased samples tooptimize diverse automated metrics of translation quality as an alternativeinference strategy to beam search. Instead of targeting the hypotheses with thehighest model probability, MBR decoding extracts the hypotheses with thehighest estimated quality. Our experiments show that the combination of aneural translation model with a neural reference-based metric, BLEURT, resultsin significant improvement in human evaluations. This improvement is obtainedwith translations different from classical beam-search output: thesetranslations have much lower model likelihood and are less favored by surfacemetrics like BLEU.

High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics