AI 中文总结
本文提出一种可调平衡方法，通过重新加权系统控制机器翻译元评估中充分性与流畅性的平衡，并设计优化算法与评分器增强框架，验证了其有效性和内部一致性。
AI 中文摘要
机器翻译元评估中存在一个权衡，即在优先考虑与充分性对齐还是流畅性之间进行选择。这种平衡取决于元评估数据集中翻译系统的组合。该系统集合是一个小型、经过筛选的样本，其特征在不同年份和语言对之间变化很大；它并不代表真实的系统分布。因此，充分性与流畅性的平衡往往缺乏代表性且容易变化。对于敏感领域，控制这种平衡至关重要。我们将这种平衡暴露为一个可调的选择。为了实现目标平衡，我们在最小化与均匀加权相比的失真的同时，对现有系统进行重新加权，确保被评估的系统保持真实且具有代表性。我们提供了一种精确的优化算法，具有理论保证和剪枝机制来计算这些权重。为了验证元评估的内部一致性，我们设计了一个评分器增强框架，为评分器建立已知的相对身份。结果表明，我们的重新加权方法有效控制了充分性与流畅性的平衡，并保持了内部一致性，优于先前的方法。最后，我们分析了流行评分器在该参数扫描中的性能。
英文摘要
There is a tradeoff in machine translation meta-evaluation between prioritizing alignment with adequacy versus fluency. The balance depends on the combination of translation systems in the meta-evaluation dataset. This system set is a small, filtered sample whose characteristics change heavily across years and language pairs; it does not represent the true system distribution. Consequently, the adequacy-fluency balance is often unrepresentative and subject to change. For sensitive domains, controlling this balance is critical. We expose this balance as a tunable choice. To achieve a target balance, we reweight existing systems while minimizing distortion from uniform weighting, ensuring the evaluated systems remain real and representative. We provide an exact optimization algorithm with theoretical guarantees and pruning mechanisms to compute these weights. To validate meta-evaluation internal consistency, we design a scorer-augmentation framework that establishes a known relative identity for the scorers. Results demonstrate that our reweighting method effectively controls the adequacy-fluency balance and preserves the internal consistency, outperforming prior approaches. Finally, we analyze the performance of popular scorers across a sweep of this parameter.
CommentsAccepted by 11th Conference on Machine Translation (WMT26)
