AI RESEARCH
Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation
arXiv CS.CL
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ArXi:2605.09098v2 Announce Type: replace 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.