Findings of the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT

Alexander Fraser

WMT 2020

We describe the WMT 2020 Shared Tasks in Unsupervised MT and Very Low
Resource Supervised MT. In both tasks, the community studied German
to/from Upper Sorbian MT, which is a very realistic machine
translation scenario (unlike the simulated scenarios used in
particular in much of the unsupervised MT work in the past). We were
able to obtain most of the digital data available for Upper Sorbian, a
minority language of Germany, which was the original motivation for
the Unsupervised MT shared task. As we were defining the task, we also
obtained a small amount of parallel data (about 60000 parallel
sentences), allowing us to offer a Very Low Supervised MT task as
well. Six primary systems participated in the unsupervised shared
task, two of these systems used additional data beyond the data
released by the organizers. Ten primary systems participated in the
very low resource supervised task.  The paper discusses the
background, presents the tasks and results, and discusses best
practices for the future.