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Quality Estimation (QE), the evaluation of machine translation output withoutthe need of explicit references, has seen big improvements in the last yearswith the use of neural metrics. In this paper we analyze the viability of usingQE metrics for filtering out bad quality sentence pairs in the training data ofneural machine translation systems~(NMT). While most corpus filtering methodsare focused on detecting noisy examples in collections of texts, usually hugeamounts of web crawled data, QE models are trained to discriminate morefine-grained quality differences. We show that by selecting the highest qualitysentence pairs in the training data, we can improve translation quality whilereducing the training size by half. We also provide a detailed analysis of thefiltering results, which highlights the differences between both approaches.

There's no Data Like Better Data: Using QE Metrics for MT Data Filtering