UTFPR at WMT 2018: Minimalistic Supervised Corpora Filtering for Machine Translation

Abstract

We present the UTFPR systems at the WMT 2018 parallel corpus filtering task. Our supervised approach discerns between good and bad translations by training classic binary classification models over an artificially produced binary classification dataset derived from a high-quality translation set, and a minimalistic set of 6 semantic distance features that rely only on easy-to-gather resources. We rank translations by their probability for the ``good'' label. Our results show that logistic regression pairs best with our approach, yielding more consistent results throughout the different settings evaluated.

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Reproductions