In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet . FusionNet is a high performing reading comprehension architecture, which was designed primarily for maximum retrieval accuracy with less regard towards computational requirements. For FastFusionNets we remove the expensive CoVe layers  and substitute the BiLSTMs with far more efficient SRU layers . The resulting architecture obtains state-of-the-art results on DAWNBench  while achieving the lowest training and inference time on SQuAD  to-date. The code is available at https://github.com/felixgwu/FastFusionNet.
translated by 谷歌翻译