USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation. (arXiv:2202.10062v2 [cs.CL] UPDATED)

The vast majority of evaluation metrics for machine translation are
supervised, i.e., (i) assume the existence of reference translations, (ii) are
trained on human scores, or (iii) leverage parallel data. This hinders their
applicability to cases where such supervision signals are not available. In
this work, we develop fully unsupervised evaluation metrics. To do so, we
leverage similarities and synergies between evaluation metric induction,
parallel corpus mining, and MT systems. In particular, we use an unsupervised
evaluation metric to mine …

arxiv evaluation machine machine translation metrics translation unsupervised