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This paper provides an overview of NVIDIA NeMo's neural machine translationsystems for the constrained data track of the WMT21 News and Biomedical SharedTranslation Tasks. Our news task submissions for English-German (En-De) andEnglish-Russian (En-Ru) are built on top of a baseline transformer-basedsequence-to-sequence model. Specifically, we use a combination of 1) checkpointaveraging 2) model scaling 3) data augmentation with backtranslation andknowledge distillation from right-to-left factorized models 4) finetuning ontest sets from previous years 5) model ensembling 6) shallow fusion decodingwith transformer language models and 7) noisy channel re-ranking. Additionally,our biomedical task submission for English-Russian uses a biomedically biasedvocabulary and is trained from scratch on news task data, medically relevanttext curated from the news task dataset, and biomedical data provided by theshared task. Our news system achieves a sacreBLEU score of 39.5 on the WMT'20En-De test set outperforming the best submission from last year's task of 38.8.Our biomedical task Ru-En and En-Ru systems reach BLEU scores of 43.8 and 40.3respectively on the WMT'20 Biomedical Task Test set, outperforming the previousyear's best submissions.

NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21