Linear-time Minimum Bayes Risk Decoding with Reference Aggregation
Jannis Vamvas Rico Sennrich
Minimum Bayes Risk (MBR) decoding is a text generation technique that has
been shown to improve the quality of machine translations, but is expensive,
even if a sampling-based approximation is used. Besides requiring a large
number of sampled sequences, it requires the pairwise calculation of a utility
metric, which has quadratic complexity. In this paper, we propose to
approximate pairwise metric scores with scores calculated against aggregated
reference representations. This changes the complexity of utility estimation
from $O(n^2)$ to $O(n)$, while empirically preserving most of the quality gains
of MBR decoding. We release our source code at https://github.com/ZurichNLP/mbr