Edinburgh’s Submission to the WMT 2022 Efficiency Task
- Nikolay Bogoychev ,
- Maximiliana Behnke ,
- J. van der Linde ,
- Graeme Nail ,
- Kenneth Heafield ,
- Biao Zhang ,
- Sidharth Kashyap
Proceedings of the Seventh Conference on Machine Translation (WMT) |
Published by Association for Computational Linguistics
We participated in all tracks of the WMT 2022 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions explores a number of several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, shortlisting, deep encoder, shallow decoder, pruning and bidirectional decoder. For the CPU track, we used quantized 8-bit models. For the GPU track, we used FP16 quantisation. We explored various pruning strategies and combination of one or more of the above methods.