EQG-RACE: Examination-Type Question Generation
This work provides a new benchmark and prototype for the Question Generation community by using a reshaped dataset and method, which is important for researchers developing intelligent tutoring systems.
The paper addresses the issue of biased and unnatural language sources in Question Generation (QG) by proposing EQG-RACE, an approach that generates exam-like questions from the RACE dataset. It employs a Rough Answer and Key Sentence Tagging scheme and an Answer-guided Graph Convolutional Network to achieve state-of-the-art performance.
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.