Scaling neural machine translation to bigger data sets with faster training and inference

What this post added

This post details significant advancements in scaling neural machine translation (NMT) by drastically reducing training and inference times. Key contributions include achieving a 45x speedup in training through optimizations like 16-bit precision, cumulative updates, and overlapping communication with backward passes. It also highlights the ability to train more accurate models using larger datasets, including monolingual data via back-translation, and improvements in translation speed. The post emphasizes the open-sourcing of the fairseq library to enable faster NMT training for the wider research community.