AI RESEARCH
PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation
arXiv CS.CL
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ArXi:2601.18006v2 Announce Type: replace We present PEAR (Pairwise Evaluation for Automatic Relative Scoring), a supervised quality estimation (QE) metric family that reframes reference-free machine translation (MT) evaluation as a graded pairwise comparison. Given a source segment and two candidate translations, PEAR predicts the direction and magnitude of their quality difference. The metrics are trained using pairwise supervision derived from differences in human judgments, with an additional regularization term that encourages sign inversion under candidate order reversal.