Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference. State-of-the-art QE systems based on pretrained language models have been achieving remarkable correlations with human judgements yet they are computationally heavy and require human annotations, which are slow and expensive to create. To address these limitations, we define the problem of metric estimation (ME) where one predicts the automated metric scores also without the reference. We show that even without access to the reference, our model can estimate automated metrics ($\rho$=60% for BLEU, $\rho$=51% for other metrics) at the sentence-level. Because automated metrics correlate with human judgements, we can leverage the ME task for pre-training a QE model. For the QE task, we find that pre-training on TER is better ($\rho$=23%) than training for scratch ($\rho$=20%).

翻译：机器翻译质量估计（QE）旨在无参考译文的情况下预测翻译假设的人工评价。基于预训练语言模型的最先进QE系统虽已实现与人工评价的显著相关性，但其计算负担沉重且依赖人工标注——后者不仅耗时且成本高昂。为解决这些局限，我们定义了指标估计（ME）问题：在无参考译文条件下预测自动化指标得分。研究表明，即使无法获取参考译文，我们的模型仍能在句子层面有效估计自动化指标（BLEU的斯皮尔曼相关系数ρ=60%，其他指标ρ=51%）。由于自动化指标与人工评价具有相关性，我们可借助ME任务对QE模型进行预训练。在QE任务中，我们发现基于TER预训练（ρ=23%）比从零训练（ρ=20%）效果更优。