  Recent advances in machine translation (MT) have shown that Minimum Bayes
Risk (MBR) decoding can be a powerful alternative to beam search decoding,
especially when combined with neural-based utility functions. However, the
performance of MBR decoding depends heavily on how and how many candidates are
sampled from the model. In this paper, we explore how different sampling
approaches for generating candidate lists for MBR decoding affect performance.
We evaluate popular sampling approaches, such as ancestral, nucleus, and top-k
sampling. Based on our insights into their limitations, we experiment with the
recently proposed epsilon-sampling approach, which prunes away all tokens with
a probability smaller than epsilon, ensuring that each token in a sample
receives a fair probability mass. Through extensive human evaluations, we
demonstrate that MBR decoding based on epsilon-sampling significantly
outperforms not only beam search decoding, but also MBR decoding with all other
tested sampling methods across four language pairs.
