EAGER: Automatic Story Generation in Support of Early Vocabulary Learning

在儿童发育过程中，早期的微小差异可能会产生巨大的长期影响。这方面的一个例子是早期词汇量、识字率和后来的学业成就之间的关系。考虑到这种关系，许多基于与照顾者共享阅读的词汇丰富计划已经开发出来，成功与否参差不齐。有证据表明，个性化的目标词汇选择可以改善学习，但手动生成包含每个孩子个性化目标词汇的故事是不可行的。使用自然语言处理技术的自动故事生成有可能解决这个问题。尽管在成人故事自动生成方面取得了一些进展，但当故事面向学龄前儿童时，这是一个尚未解决的问题，尤其是具有挑战性的问题，因为内容和复杂性都需要针对年龄段量身定做。因此，研究人员探索了多种创新的机器学习方法，以生成引人入胜的、高质量的儿童故事，这些故事包含特定的单词，将丰富儿童的词汇。此外，学龄前儿童和他们的照顾者参与故事分享活动，以调查自动生成的故事是否是向儿童教授单词的有效工具。这项研究对低收入家庭和双语学习者尤其重要，他们更有可能出现词汇延迟，同时不太可能得到干预支持。这项早期探索性研究赠款通过采取必要的探索性步骤，朝着灵活、适应性的技术方向发展，自动生成个性化、引人入胜和有效的故事，供幼儿和他们的照顾者在家中分享，作为早期词汇丰富的工具，从而在自动故事生成领域做出了新的、潜在的变革性贡献。具体地说，本项目的第一部分包括以下内容：1)调查多种计算模型是否适合学龄前儿童指导的故事生成；2)研究避免基于机器学习的故事生成模型生成不适合儿童的内容的策略；以及3)探索如何将一组预定义的目标词自动纳入生成的故事中。此外，研究团队通过以下方式调查故事生成模型的质量和故事在单词学习中的有效性：4)从当地社区的家庭那里获得反馈，了解自动生成的故事是否适合学龄前儿童并吸引他们；5)进行实验室研究，在类似于自然家庭环境的环境中由照顾者及其孩子分享故事，然后将儿童对目标单词的知识与控制单词进行比较。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In child development, small early differences can compound into big long-term effects. One example of this is the relationship between early vocabulary size, literacy, and later academic achievement. With this relationship in mind, many vocabulary enrichment programs based on shared reading with a caregiver have been developed, with mixed success. Evidence suggests that individualizing target-vocabulary selection can improve learning, but manually generating stories that include personalized target words for every child is infeasible. Automatic story generation using natural language processing techniques has the potential to solve this problem. Although there has been some progress in automatic story generation for adults, this is an unsolved and particularly challenging problem when stories are targeted for preschoolers, because both content and complexity need to be tailored to the age group. Thus, the researchers explore multiple innovative machine learning methods to generate engaging, high-quality child-directed stories that contain specific words that will enrich a child’s vocabulary. Furthermore, preschoolers and their caregivers participate in story-sharing activities to investigate if the automatically generated stories are effective tools for teaching words to children. This research is particularly critical for low-income families and dual language learners, who are more likely to exhibit vocabulary delays while, at the same time, being less likely to receive intervention support.This EArly Grant for Exploratory Research makes novel and potentially transformative contributions to the area of automatic story generation by taking necessary exploratory steps towards flexible, adaptive technology that can automatically generate personalized, engaging, and effective stories for toddlers and their caregivers to share at home as a vehicle for early vocabulary enrichment. Specifically, the first part of this project consists of the following: 1) an investigation of multiple computational models with regards to their suitability for preschooler-directed story generation; 2) a study of strategies to avoid the generation of content that is not suitable for children by machine learning-based story generation models; and 3) an exploration of how to automatically incorporate a set of predefined target words into generated stories. Furthermore, the team of researchers investigates the quality of story generation models and the stories' effectiveness for word learning via the following: 4) obtaining feedback from families in the local community as to whether the automatically generated stories are appropriate and engaging for preschoolers and 5) conducting a laboratory study in which stories will be shared by caregivers and their children in a setting that resembles a natural home environment and subsequently comparing the children’s knowledge of target words against control words.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.