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Abstract
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 whitespace-delimited source words per second on a single CPU thread, thus making neural translation feasible on consumer hardware without a GPU.
Original language | English |
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Title of host publication | Proceedings of the Fifth Conference on Machine Translation |
Place of Publication | Online |
Publisher | Association for Computational Linguistics |
Pages | 191-196 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-948087-81-0 |
Publication status | Published - 19 Nov 2020 |
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Dive into the research topics of 'Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task'. Together they form a unique fingerprint.Projects
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