EQG-RACE: Examination-Type Question Generation
AbstractQuestion 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.
How to Cite
Jia, X., Zhou, W., Sun, X., & Wu, Y. (2021). EQG-RACE: Examination-Type Question Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 13143-13151. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17553
AAAI Technical Track on Speech and Natural Language Processing I