论文标题
加速实时问题通过问题产生回答
Accelerating Real-Time Question Answering via Question Generation
论文作者
论文摘要
尽管深层神经网络在问答（QA）方面取得了巨大的成功，但它们仍处于重大计算和能源成本的实际产品部署。此外，现有的质量检查系统通过神经网络的实时问题的编码时间瓶颈，因此在大量流量的部署中遭受了可检测到的延迟。为了减少用于实际用法的计算成本和加速实时问答（RTQA），我们建议从在线QA系统中删除所有神经网络，并呈现Ocean-Q（一个问题海洋），该QA生成（QG）模型介绍了一个QA Pairs Pairs yourtime Qa pairs youtsime QA，然后在无线问题上与QA进行QA的QA量相匹配，而不必与Contectic posed posed posed posect anduts complate andutical Quants consod compod andicaties consod todic consod todics consod tote todics consod。 Ocean-Q可以容易地部署在现有的分布式数据库系统或搜索引擎中，以供大规模查询使用，并且更绿色，而没有维护大型神经网络的额外费用。在小队（ - 开放）和HOTPOTQA基准测试的实验表明，Ocean-Q能够将最快的最新RTQA系统加速4倍，而精度仅为3+％。
Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deployment. Further, existing QA systems are bottlenecked by the encoding time of real-time questions with neural networks, thus suffering from detectable latency in deployment for large-volume traffic. To reduce the computational cost and accelerate real-time question answering (RTQA) for practical usage, we propose to remove all the neural networks from online QA systems, and present Ocean-Q (an Ocean of Questions), which introduces a new question generation (QG) model to generate a large pool of QA pairs offline, then in real time matches an input question with the candidate QA pool to predict the answer without question encoding. Ocean-Q can be readily deployed in existing distributed database systems or search engine for large-scale query usage, and much greener with no additional cost for maintaining large neural networks. Experiments on SQuAD(-open) and HotpotQA benchmarks demonstrate that Ocean-Q is able to accelerate the fastest state-of-the-art RTQA system by 4X times, with only a 3+% accuracy drop.
