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    <abstract>This paper introduces Pairwise Difference Pearson (PDP), a novel segment-level meta-evaluation metric for Machine Translation (MT) that addresses limitations in previous Pearson’s ρ-based and Kendall’s τ-based meta-evaluation approaches. PDP is a correlation-based 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 only pairwise differences to refine Global Pearson to intra-segment comparisons. Analysis on the WMT’24 shared task shows PDP properly ranks sentinel evaluation metrics and better aligns with human error weightings than acc_eq.</abstract>
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%0 Conference Proceedings
%T Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation
%A DiIanni, Colten
%A Deutsch, Daniel
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F diianni-deutsch-2025-dont
%X This paper introduces Pairwise Difference Pearson (PDP), a novel segment-level meta-evaluation metric for Machine Translation (MT) that addresses limitations in previous Pearson’s ρ-based and Kendall’s τ-based meta-evaluation approaches. PDP is a correlation-based 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 only pairwise differences to refine Global Pearson to intra-segment comparisons. Analysis on the WMT’24 shared task shows PDP properly ranks sentinel evaluation metrics and better aligns with human error weightings than acc_eq.
%R 10.18653/v1/2025.emnlp-main.1273
%U https://aclanthology.org/2025.emnlp-main.1273/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1273
%P 25062-25070

Markdown (Informal)

[Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation](https://aclanthology.org/2025.emnlp-main.1273/) (DiIanni & Deutsch, EMNLP 2025)

• Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation (DiIanni & Deutsch, EMNLP 2025)

ACL

• Colten DiIanni and Daniel Deutsch. 2025. Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 25062–25070, Suzhou, China. Association for Computational Linguistics.