Applying Information Extraction to Storybook Question and Answer Generation
Kai-Yen Kao | Chia-Hui Chang
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
For educators, how to generate high quality question-answer pairs from story text is a time-consuming and labor-intensive task. The purpose is not to make students unable to answer, but to ensure that students understand the story text through the generated question-answer pairs. In this paper, we improve the FairyTaleQA question generation method by incorporating question type and its definition to the input for fine-tuning the BART (Lewis et al., 2020) model. Furthermore, we make use of the entity and relation extraction from (Zhong and Chen, 2021) as an element of template-based question generation.