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）无需参考译文即可预测翻译假设的人工评判。基于预训练语言模型的最先进QE系统已实现与人工评判的高度相关性，但其计算开销巨大且依赖人工标注——这种标注既缓慢又昂贵。为解决这些局限，我们定义了指标估计（ME）问题：无需参考译文即可预测自动评估指标分数。实验表明，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指标（BLEU的$\rho$=60%，其他指标的$\rho$=51%）。由于自动指标与人工评判存在相关性，我们可利用ME任务预训练QE模型。在QE任务中，我们发现基于TER的预训练（$\rho$=23%）优于从零训练（$\rho$=20%）。