Edinburgh’s Submissions to the 2020 Machine Translation Efficiency Task
Nikolay Bogoychev | Roman Grundkiewicz | Alham Fikri Aji | Maximiliana Behnke | Kenneth Heafield | Sidharth Kashyap | Emmanouil-Ioannis Farsarakis | Mateusz Chudyk
Proceedings of the Fourth Workshop on Neural Generation and Translation
We participated in all tracks of the Workshop on Neural Generation and Translation 2020 Efficiency Shared Task: single-core CPU, multi-core CPU, and GPU. At the model level, we use teacher-student training with a variety of student sizes, tie embeddings and sometimes layers, use the Simpler Simple Recurrent Unit, and introduce head pruning. On GPUs, we used 16-bit floating-point tensor cores. On CPUs, we customized 8-bit quantization and multiple processes with affinity for the multi-core setting. To reduce model size, we experimented with 4-bit log quantization but use floats at runtime. In the shared task, most of our submissions were Pareto optimal with respect the trade-off between time and quality.
- Nikolay Bogoychev 1
- Roman Grundkiewicz 1
- Alham Fikri Aji 1
- Maximiliana Behnke 1
- Kenneth Heafield 1
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