  Recent research in decoding methods for Natural Language Generation (NLG)
tasks has shown that MAP decoding is not optimal, because model probabilities
do not always align with human preferences. Stronger decoding methods,
including Quality Estimation (QE) reranking and Minimum Bayes' Risk (MBR)
decoding, have since been proposed to mitigate the model-perplexity-vs-quality
mismatch. While these decoding methods achieve state-of-the-art performance,
they are prohibitively expensive to compute. In this work, we propose MBR
finetuning and QE finetuning which distill the quality gains from these
decoding methods at training time, while using an efficient decoding algorithm
at inference time. Using the canonical NLG task of Neural Machine Translation
(NMT), we show that even with self-training, these finetuning methods
significantly outperform the base model. Moreover, when using an external LLM
as a teacher model, these finetuning methods outperform finetuning on
human-generated references. These findings suggest new ways to leverage
monolingual data to achieve improvements in model quality that are on par with,
or even exceed, improvements from human-curated data, while maintaining maximum
efficiency during decoding.
