Accelerating Real-Time Question Answering via Question Generation
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by
Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, Jingjing Liu
2020
Abstract
Existing approaches to real-time question answering (RTQA) rely on learning
the representations of only key phrases in the documents, then matching them
with the question representation to derive answer. However, such approach is
bottlenecked by the encoding time of real-time questions, thus suffering from
detectable latency in deployment for large-volume traffic. To accelerate RTQA
for practical use, we present Ocean-Q (an Ocean of Questions), a novel approach
that leverages question generation (QG) for RTQA. Ocean-Q introduces a QG model
to generate a large pool of question-answer (QA) pairs offline, then in real
time matches an input question with the candidate QA pool to predict the answer
without question encoding. To further improve QG quality, we propose a new data
augmentation method and leverage multi-task learning and diverse beam search to
boost RTQA performance. 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.
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