  We participated in the WMT 2022 Large-Scale Machine Translation Evaluation
for the African Languages Shared Task. This work describes our approach, which
is based on filtering the given noisy data using a sentence-pair classifier
that was built by fine-tuning a pre-trained language model. To train the
classifier, 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 by
choosing sentences with low alignment scores. Our final machine translation
model was then trained on filtered data, instead of the entire noisy dataset.
We empirically validate our approach by evaluating on two common datasets and
show that data filtering generally improves overall translation quality, in
some cases even significantly.
