In this paper, we present a coarse to fine question answering (CFQA) system
based on reinforcement learning which can efficiently processes documents with
different lengths by choosing appropriate actions. The system is designed using
an actor-critic based deep reinforcement learning model to achieve multi-step
question answering. Compared to previous QA models targeting on datasets mainly
containing either short or long documents, our multi-step coarse to fine model
takes the merits from multiple system modules, which can handle both short and
long documents. The system hence obtains a much better accuracy and faster
trainings speed compared to the current state-of-the-art models. We test our
model on four QA datasets, WIKEREADING, WIKIREADING LONG, CNN and SQuAD, and
demonstrate 1.3$\%$-1.7$\%$ accuracy improvements with 1.5x-3.4x training
speed-ups in comparison to the baselines using state-of-the-art models.

本文提出了一种基于强化学习的粗到精问答（CFQA）系统，使用多步骤的深度强化学习模型来处理文档，可以处理较短或较长的文档，相较于先前的 QA 模型，在 WIKEREADING、WIKIREADINGLONG、CNN 和 SQuAD 等四个 QA 数据集上得到了 1.3%-1.7% 的准确率和 1.5 倍至 3.4 倍的训练速度改善。