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Accelerating Real-Time Question Answering via Question Generation
[article]
2021
arXiv
pre-print
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
arXiv:2009.05167v2
fatcat:sqy6s5f2jvbyfpltataeayoozm