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Transformer based Very Large Language Models (VLLMs) like BERT, XLNet andRoBERTa, have recently shown tremendous performance on a large variety ofNatural Language Understanding (NLU) tasks. However, due to their size, theseVLLMs are extremely resource intensive and cumbersome to deploy at productiontime. Several recent publications have looked into various ways to distilknowledge from a transformer based VLLM (most commonly BERT-Base) into asmaller model which can run much faster at inference time. Here, we propose anovel set of techniques which together produce a task-specific hybridconvolutional and transformer model, WaLDORf, that achieves state-of-the-artinference speed while still being more accurate than previous distilled models.

WaLDORf: Wasteless Language-model Distillation On Reading-comprehension