EAGER: Automatic Story Generation in Support of Early Vocabulary Learning

在儿童的发展过程中，早期的小差异可以复合成巨大的长期影响。这方面的一个例子是早期词汇量，识字率和后来的学术成就之间的关系。考虑到这种关系，已经开发了许多基于与护理者共享阅读的词汇丰富程序，取得了不同的成功。有证据表明，个性化的目标词汇选择可以改善学习，但手动生成的故事，包括个性化的目标词为每个孩子是不可行的。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，但当故事针对学龄前儿童时，这是一个未解决的特别具有挑战性的问题，因为内容和复杂性都需要针对年龄组进行定制。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、高质量的儿童指导故事，这些故事包含丰富儿童词汇的特定词汇。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教孩子单词的有效工具。这项研究对于低收入家庭和双语学习者来说尤其重要，他们更有可能表现出词汇延迟，同时，不太可能接受干预支持。EArly探索性研究基金通过采取必要的探索性步骤，自适应技术，可以自动生成个性化的，引人入胜的，有效的故事，幼儿和他们的照顾者在家里分享作为早期词汇丰富的工具。具体而言，本项目的第一部分包括以下内容：1）调查多种计算模型对学龄前儿童指导的故事生成的适用性; 2）研究基于机器学习的故事生成模型，以避免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地并入到生成的故事中。此外，研究小组还通过以下方式调查了故事生成模型的质量和故事对单词学习的有效性：4）从当地社区的家庭获得反馈，以确定自动生成的故事是否适合学龄前儿童，以及5）进行一项实验室研究，其中故事将由照顾者及其子女在类似于自然家庭环境的环境中分享，这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.