Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task
Ulrich Germann, Roman Grundkiewicz, Martin Popel, Radina Dobreva, Nikolay Bogoychev, Kenneth Heafield · 2020
We describe the joint submission of the University of Edinburgh and Charles University, Prague, to the Czech/English track in the WMT 2020 Shared Task on News Translation.Our fast and compact student models distill knowledge from a larger, slower teacher.They are designed to offer a good trade-off between translation quality and inference efficiency.On the WMT 2020 Czech ↔ English test sets, they achieve translation speeds of over 700 whitespacedelimited source words per second 1 on a single CPU thread, thus making neural translation feasible on consumer hardware without a GPU.