Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural metrics. In this paper we analyze the viability of using QE metrics for filtering out bad quality sentence pairs in the training data of neural machine translation systems~(NMT). While most corpus filtering methods are focused on detecting noisy examples in collections of texts, usually huge amounts of web crawled data, QE models are trained to discriminate more fine-grained quality differences. We show that by selecting the highest quality sentence pairs in the training data, we can improve translation quality while reducing the training size by half. We also provide a detailed analysis of the filtering results, which highlights the differences between both approaches.

翻译：质量估计（QE）是一种无需显式参考即可评估机器翻译输出的技术，近年来随着神经度量指标的使用取得了显著进展。本文分析了利用质量估计指标过滤神经机器翻译系统（NMT）训练数据中低质量句对的可行性。虽然大多数语料过滤方法专注于从文本集合（通常来自海量网络爬取数据）中检测噪声样本，但质量估计模型经过训练可区分更细粒度的质量差异。我们证明，通过选择训练数据中最高质量的句对，即使将训练规模缩减一半，也能提升翻译质量。我们还提供了过滤结果的详细分析，揭示了这两种方法之间的差异。