  We present the contribution of the Unbabel team to the WMT 2020 Shared Task
on Metrics. We intend to participate on the segment-level, document-level and
system-level tracks on all language pairs, as well as the 'QE as a Metric'
track. Accordingly, we illustrate results of our models in these tracks with
reference to test sets from the previous year. Our submissions build upon the
recently proposed COMET framework: We train several estimator models to regress
on different human-generated quality scores and a novel ranking model trained
on relative ranks obtained from Direct Assessments. We also propose a simple
technique for converting segment-level predictions into a document-level score.
Overall, our systems achieve strong results for all language pairs on previous
test sets and in many cases set a new state-of-the-art.
