Facebook FAIR's WMT19 news translation task submission

Abstract

This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in four language directions, English ↔ German and English ↔ Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system's performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English→Russian.