Question Generation (QG) is an essential component of the automatic
intelligent tutoring systems, which aims to generate high-quality questions for
facilitating the reading practice and assessments. However, existing QG
technologies encounter several key issues concerning the biased and unnatural
language sources of datasets which are mainly obtained from the Web (e.g.
SQuAD). In this paper, we propose an innovative Examination-type Question
Generation approach (EQG-RACE) to generate exam-like questions based on a
dataset extracted from RACE. Two main strategies are employed in EQG-RACE for
dealing with discrete answer information and reasoning among long contexts. A
Rough Answer and Key Sentence Tagging scheme is utilized to enhance the
representations of input. An Answer-guided Graph Convolutional Network (AG-GCN)
is designed to capture structure information in revealing the inter-sentences
and intra-sentence relations. Experimental results show a state-of-the-art
performance of EQG-RACE, which is apparently superior to the baselines. In
addition, our work has established a new QG prototype with a reshaped dataset
and QG method, which provides an important benchmark for related research in
future work. We will make our data and code publicly available for further
research.

本研究提出一种基于 RACE 数据集的创新型考试问答生成方法 (EQG-RACE)，其利用了答案导向的图卷积网络来增强问答输入的表示，解决了已有问答生成技术中存在的因来自 Web 的偏向性和不自然语言 sources 导致的问题。实验结果表明 EQG-RACE 性能卓越，为后续相关研究提供了新的基准。