Generate Me a Bedtime Story: Leveraging Natural Language Processing for Early Vocabulary Enhancement

Trevor Hall, Maria Valentini, Eliana Colunga, Katharina Kann

Abstract: A young child’s vocabulary size is correlated with their level of personal wellbeing and future academic success. Yet, interventions aimed at increasing early vocabulary would ideally be tailored to each individual child’s needs and interests, and such personalization would be impossible without technological support. Here, we explore if and how natural language processing can be used to create individual- ized bedtime stories around target words to be learned by preschoolers. Generating stories from scratch is challenging and often results in stories of low quality. Thus, we propose an alternative approach: completing phrase-level gaps within prewritten stories. On this task, we explore the performance of GPT-3 with and without finetuning as well as with and without providing a word which is semantically related to the target word. Manual evaluation of the generated stories shows that GPT-3 and GPT-3- based models perform well on the task. Using GPT-3 without finetuning and including a con- text word into the prompt is the best performing approach.