StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children's Story-Based Learning

TL;DR

StorySparkQA introduces a new dataset of 5,868 expert-annotated QA pairs that incorporate real-world knowledge into children's story-based learning, addressing limitations of existing datasets.

Contribution

The paper presents an annotation framework and dataset that capture expert thinking and real-world knowledge for improved educational QA systems.

Findings

Effective support for models generating real-world knowledge QA pairs

Automated and human evaluations validate dataset quality

Enhances interactive story reading with expert-annotated knowledge

Abstract

Interactive story reading is a common parent-child activity, where parents expect to teach both language skills and real-world knowledge beyond the story. While increasing storytelling and reading systems have been developed for this activity, they often fail to infuse real-world knowledge into the conversation. This limitation can be attributed to the existing question-answering (QA) datasets used for children's education, upon which the systems are built, failing to capture the nuances of how education experts think when conducting interactive story reading activities. To bridge this gap, we design an annotation framework, empowered by existing knowledge graph to capture experts' annotations and thinking process, and leverage this framework to construct StorySparkQA dataset, which comprises 5,868 expert-annotated QA pairs with real-world knowledge. We conduct automated and human expert evaluations across various QA pair generation settings to demonstrate that our StorySparkQA can effectively support models in generating QA pairs that target real-world knowledge beyond story content. StorySparkQA is available at https://huggingface.co/datasets/NEU-HAI/StorySparkQA.