穷人的质量估计：在没有参考译文的情况下预测基于参考的机器翻译指标
Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference
AI总结：
本文提出无参考译文的指标估计（ME）任务，用模型预测 BLEU、TER 等自动指标分数，并以此预训练 QE 模型，在降低标注与计算成本的同时提升机器翻译质量估计效果。
摘要：
机器翻译质量估计（QE）旨在不查看参考译文的情况下，预测人类对翻译候选译文的判断。基于预训练语言模型的最先进 QE 系统已经能够与人类判断取得显著相关性，但它们计算开销很大，并且需要人工标注，而标注的制作既缓慢又昂贵。为解决这些限制，我们定义了指标估计（ME）问题，即同样在没有参考译文的情况下预测自动化指标分数。我们表明，即使无法获取参考译文，我们的模型仍能在句子层面估计自动化指标（BLEU 的 ρ=60%，其他指标的 ρ=51%）。由于自动化指标与人类判断相关，我们可以利用 ME 任务来预训练 QE 模型。对于 QE 任务，我们发现基于 TER 进行预训练（ρ=23%）优于从头开始训练（ρ=20%）。
英文摘要：
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 ($ρ$=60% for BLEU, $ρ$=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 ($ρ$=23%) than training for scratch ($ρ$=20%).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
