EAGER: Automatic Story Generation in Support of Early Vocabulary Learning

EAGER：自动故事生成支持早期词汇学习

• 批准号: 2223917
• 负责人: Katharina von der Wense
• 金额: $ 29.97万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223917&HistoricalAwards=false
• 关键词: EAGER; Automatic; Story; Generation; Support

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.

在儿童发展中，小早期差异可能会加剧长期影响。一个例子是早期词汇量，识字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人员共享阅读的词汇丰富计划，并取得了不同的成功。有证据表明，个体化的目标唱歌量可以改善学习，但是手动产生的故事包括每个孩子的个性化目标是不可行的。使用自然语言处理技术的自动故事产生有可能解决此问题。尽管成人的自动故事产生已经取得了一些进展，但是当故事针对学龄前儿童时，这是一个未解决的问题，尤其是挑战的问题，因为内容和复杂性都需要针对年龄段量身定制。这是研究人员探索多种创新的机器学习方法，以产生引人入胜的高质量儿童指导的故事，这些故事包含特定的单词，这些词将丰富儿童的词汇。此外，学龄前儿童及其护理人员参加了故事共享活动，以调查自动产生的故事是否是向儿童讲单词的有效工具。 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作为早期词汇丰富的车辆。具体而言，该项目的第一部分包括以下内容：1）多个计算模型的投资，以适合学龄前儿童的故事生成； 2）避免通过基于机器学习的故事生成模型不适合儿童的内容的策略的研究； 3）探索如何自动将一组预定义的目标词纳入生成的故事中。此外，研究人员团队还调查了故事模型的质量以及通过以下以下方式调查单词学习的有效性：4）从当地社区中的家庭中获取反馈，以了解自动产生的故事是否适当，并且对学龄前儿童以及进行实验室研究，并进行一项实验室研究，以言语与孩子们在自然环境中分享一句话，并在自然界中分享一个故事，并在自然界中分享言语，并以自然的环境相提并论。 NSF的法定使命，并通过评估诚实地认为，使用基金会的知识分子优点和更广泛的影响审查标准。

项目成果

On the Automatic Generation and Simplification of Children’s Stories

论儿童故事的自动生成与简化

• DOI: 10.18653/v1/2023.emnlp-main.218
• 发表时间: 2023
• 期刊: Association for Computational Linguistics
• 作者: Valentini, Maria;Weber, Jennifer;Salcido, Jesus;Wright, Téa;Colunga, Eliana;von der Wense, Katharina
• 通讯作者: von der Wense, Katharina