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We participated in the WMT 2022 Large-Scale Machine Translation Evaluationfor the African Languages Shared Task. This work describes our approach, whichis based on filtering the given noisy data using a sentence-pair classifierthat was built by fine-tuning a pre-trained language model. To train theclassifier, we obtain positive samples (i.e. high-quality parallel sentences)from a gold-standard curated dataset and extract negative samples (i.e.low-quality parallel sentences) from automatically aligned parallel data bychoosing sentences with low alignment scores. Our final machine translationmodel was then trained on filtered data, instead of the entire noisy dataset.We empirically validate our approach by evaluating on two common datasets andshow that data filtering generally improves overall translation quality, insome cases even significantly.

Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages