  Automatic machine translation metrics typically rely on human translations to
determine the quality of system translations. Common wisdom in the field
dictates that the human references should be of very high quality. However,
there are no cost-benefit analyses that could be used to guide practitioners
who plan to collect references for machine translation evaluation. We find that
higher-quality references lead to better metric correlations with humans at the
segment-level. Having up to 7 references per segment and taking their average
(or maximum) helps all metrics. Interestingly, the references from vendors of
different qualities can be mixed together and improve metric success. Higher
quality references, however, cost more to create and we frame this as an
optimization problem: given a specific budget, what references should be
collected to maximize metric success. These findings can be used by evaluators
of shared tasks when references need to be created under a certain budget.
