1 research outputs found
Facebook FAIR's WMT19 News Translation Task Submission
This paper describes Facebook FAIR's submission to the WMT19 shared news
translation task. We participate in two language pairs and four language
directions, English German and English Russian. Following our
submission from last year, our baseline systems are large BPE-based transformer
models trained with the Fairseq sequence modeling toolkit which rely on sampled
back-translations. 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 submissions are ranked first in all four
directions of the human evaluation campaign. On En->De, our system
significantly outperforms other systems as well as human translations. This
system improves upon our WMT'18 submission by 4.5 BLEU points.Comment: 7 pages; WM