We propose Dynamic Meta-Metrics (DMM), a framework for machine translation evaluation that learns source-sentence conditioned combinations of existing metrics. Rather than relying on a single static ensemble or language-specific weighting, DMM adapts the metric combination based on properties of the source segment. We study hard conditioning, which fits an interpretable combiner per cluster, and an exploratory soft-conditioned extension whose weights vary continuously with source-cluster responsibilities. We evaluate DMM on the WMT Metrics Shared Task data across multiple language pairs using pairwise agreement measures at the system and segment levels. Across settings, MLP-based combinations outperform linear and Gaussian process-based ensembles, and introducing soft conditioning yields gains over linear models.
@article{arxiv.2605.09098,
title = {Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation},
author = {Luke Zhang and Justin Vasselli and Aditya Khan and York Hay Ng and En-Shiun Annie Lee},
journal= {arXiv preprint arXiv:2605.09098},
year = {2026}
}