  Large web-crawled corpora represent an excellent resource for improving the
performance of Neural Machine Translation (NMT) systems across several language
pairs. However, since these corpora are typically extremely noisy, their use is
fairly limited. Current approaches to dealing with this problem mainly focus on
filtering using heuristics or single features such as language model scores or
bi-lingual similarity. This work presents an alternative approach which learns
weights for multiple sentence-level features. These feature weights which are
optimized directly for the task of improving translation performance, are used
to score and filter sentences in the noisy corpora more effectively. We provide
results of applying this technique to building NMT systems using the Paracrawl
corpus for Estonian-English and show that it beats strong single feature
baselines and hand designed combinations. Additionally, we analyze the
sensitivity of this method to different types of noise and explore if the
learned weights generalize to other language pairs using the Maltese-English
Paracrawl corpus.
