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arXiv · 1902.11291

FastFusionNet: New State-of-the-Art for DAWNBench SQuAD

Felix Wu·Boyi Li·Lequn Wang·Ni Lao·John Blitzer·Kilian Q. Weinberger
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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.

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Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger. 2019-02-28. FastFusionNet: New State-of-the-Art for DAWNBench SQuAD. https://arxiv.org/abs/1902.11291

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