Parker Riley, Daniel Deutsch, Mara Finkelstein, Colten DiIanni, Juraj Juraska, Markus Freitag. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper introduces Pairwise Difference Pearson (PDP), a novel segment-level metaevaluation metric for Machine Translation (MT) that address limitations in previous Pearson's ρ-based and and Kendall's τ -based metaevaluation approaches.PDP is a correlationbased metric that utilizes pairwise differences rather than raw scores.It draws on information from all segments for a more robust understanding of score distributions and uses segmentwise pairwise differences to refine Global Pearson to intra-segment score comparisons.Analysis on the WMT'24 shared task shows PDP properly ranks sentinel evaluation metrics and better aligns with human error weightings than previous work.Noise injection analysis demonstrates PDP's robustness to random noise, segment bias, and system bias while highlighting its sensitivity to extreme outliers.