Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022)

Abstract: Generating educational questions of fairytales or storybooks is vital for improving children’s literacy ability. However, it is challenging to generate questions that capture the interesting aspects of a fairytale story with educational meaningfulness. In this paper, we propose a novel question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. To train the event-centric summarizer, we finetune a pre-trained transformer-based sequence-to-sequence model using silver samples composed by educational question-answer pairs. On a newly proposed educational question-answering dataset FairytaleQA, we show good performance of our method on both automatic and human evaluation metrics. Our work indicates the necessity of decomposing question type distribution learning and event-centric summary generation for educational question generation.

## introduction listening to and understanding fairy tales or storybooks are very crucial for children's early intellectual and literacy development @xcite . during the storybook reading process, prompting suitable questions with educational purposes can help children understand the content and inspire their interests @xcite . there is evidence that high-cognitive-demand (hcd) questions usually relate to good learning achievement @xcite . hcd questions usually correspond to application, analysis, synthesis, and evaluation questions in bloom's taxonomy of cognitive process @xcite , which are salient events merged from different elements across a session @xcite . however, it is challenging even for humans to ask educationally meaningful questions to engage children in storybook reading, which could be due to adults lacking the skills or time to integrate such interactive opportunities @xcite . recent research shows that ai-powered conversational agents can play the role of language partners to read fairy tales to children and ask them educational questions @xcite . this motivates us to investigate techniques to generate hcd educational questions for children's storybooks automatically. automating the generation of such questions can have great value in supporting children's language development through guided conversation. during storybook reading, hcd questions require children to make inferences and predictions. in contrast to low-cognitive-demand (lcd) questions describing facts in stories (e.g., who is snow white's mother?), hcd questions are often related to events and their relations (e.g., why did the queen want to kill snow white? or what happened after the huntsman raised his dagger in the forest?). ## method the overview of our educational question generation system for storybooks is shown in figure 1 , which contains three modules: question type distribution learning, event-centric summary generation, and educational question generation. given an input paragraph d, we first predict the type distribution of output questions @xmath0, p 2 , . . . , p t ), where p i denotes the probability of question type i, t is the total number of question types. we then transform the distribution into the number of questions under each question type @xmath1, l 2 , . . . , l t ). afterwards, we first generate l i summaries of type i with the input paragraph d, and then generate l i questions of type i with the corresponding summaries. ## experimental setup to demonstrate the effectiveness of our proposed method, we conducted a set of experiments on the fairytaleqa dataset. ## system analysis to further investigate the effectiveness of our method, we conducted a set of ablation studies. ## conclusion in this paper, we propose a novel method for educational question generation for fairy tales, which can potentially be used in early childhood education. our method contains three modules: question type distribution learning, event-centric summary generation, and educational question generation. through question type distribution learning, we can decompose the challenges of educational question generation by extracting related events of one question type and generating educational questions with a short event-centric summary, which improves the performance significantly. on both automatic evaluation and human evaluation, we show the potential of our method. in the future, we plan to further investigate the event-centric summary generation module by considering discourse-level information to improve the summarization performance and improve the factuality error problem. we are also interested in deploying the system in real scenarios to benefit childcare-related domains. the question type distribution of fairytaleqa is shown in figure 2 . in particular, question types action, causal relationship, outcome resolution, and prediction are considered as hcd questions. for each question type, there are some questions that span multiple paragraphs (denoted as spanning, character: 53, setting: 11, feeling: 59, action: 143, causal relationship: 392, outcome resolution: 54, prediction: 266), which are discarded. we select question types action, causal relationship, and outcome resolution from fairytaleqa for conducting our experiments. in total, there are 2998 out of 4095 paragraphs used, including 2430 out of 3350 training paragraphs, 290 out of 380 validation paragraphs, and 278 out of 365 paragraphs. the number of qa pairs for each question type and the total number are shown in