@inproceedings{kao-chang-2022-applying,
    title = "Applying Information Extraction to Storybook Question and Answer Generation",
    author = "Kao, Kai-Yen  and
      Chang, Chia-Hui",
    editor = "Chang, Yung-Chun  and
      Huang, Yi-Chin",
    booktitle = "Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)",
    month = nov,
    year = "2022",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.rocling-1.36/",
    pages = "289--298",
    language = "zho",
    abstract = "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."
}