  Building a deep learning model for a Question-Answering (QA) task requires a
lot of human effort, it may need several months to carefully tune various model
architectures and find a best one. It's even harder to find different excellent
models for multiple datasets. Recent works show that the best model structure
is related to the dataset used, and one single model cannot adapt to all tasks.
In this paper, we propose an automated Question-Answering framework, which
could automatically adjust network architecture for multiple datasets. Our
framework is based on an innovative evolution algorithm, which is stable and
suitable for multiple dataset scenario. The evolution algorithm for search
combine prior knowledge into initial population and use a performance estimator
to avoid inefficient mutation by predicting the performance of candidate model
architecture. The prior knowledge used in initial population could improve the
final result of the evolution algorithm. The performance estimator could
quickly filter out models with bad performance in population as the number of
trials increases, to speed up the convergence. Our framework achieves 78.9 EM
and 86.1 F1 on SQuAD 1.1, 69.9 EM and 72.5 F1 on SQuAD 2.0. On NewsQA dataset,
the found model achieves 47.0 EM and 62.9 F1.
