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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.09098 |
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| _version_ | 1866916071751024640 |
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| author | Zhang, Luke Vasselli, Justin Khan, Aditya Ng, York Hay Lee, En-Shiun Annie |
| author_facet | Zhang, Luke Vasselli, Justin Khan, Aditya Ng, York Hay Lee, En-Shiun Annie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09098 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation Zhang, Luke Vasselli, Justin Khan, Aditya Ng, York Hay Lee, En-Shiun Annie Computation and Language 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. |
| title | Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.09098 |