根据源句特点动态调整翻译评估指标权重，提升评估准确性。
Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation
- 基于源句特征动态调节现有评估指标的组合权重。
- 在多语言对上，基于MLP的组合方法优于线性与高斯过程模型。
- 软性条件化设计使评估结果更优，适合需要精准评估的场景。
我们提出动态元度量（DMM），一种机器翻译评估框架，通过学习源句特征条件下的现有指标组合来提升评估性能。不同于依赖单一静态集成或语言特异性权重的方法，DMM根据源句片段的属性自适应调整指标组合方式。研究包含硬性条件化（为每个聚类拟合可解释的组合器）和探索性软性条件化扩展（权重随源句聚类责任连续变化）。在多个语言对的WMT评估任务中，使用成对一致性指标在系统级与段级进行评估。结果显示，基于MLP的组合方法在所有设置下均优于线性及高斯过程模型，引入软性条件化进一步提升了性能。
原文摘要 · Abstract (English)
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.
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