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.

本文提出了一种基于创新的进化算法的自动问答框架，适用于多个数据集，并能自动调整网络架构，从而找到最佳解。在 SQuAD 1.1 和 SQuAD 2.0 数据集上，我们的框架实现了 78.9 EM 和 86.1 F1 以及 69.9 EM 和 72.5 F1 的效果，在 NewsQA 数据集上，实现了 47.0 EM 和 62.9 F1 的效果。