FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/felixgwu/FastFusionNetOfficialIn paperpytorch★ 0
- github.com/yellowpsyduck/OccamFusionNetpytorch★ 0
Abstract
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12]. 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 [21] and substitute the BiLSTMs with far more efficient SRU layers [19]. The resulting architecture obtains state-of-the-art results on DAWNBench [5] while achieving the lowest training and inference time on SQuAD [25] to-date. The code is available at https://github.com/felixgwu/FastFusionNet.