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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.09756 |
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| _version_ | 1866916422540591104 |
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| author | Chen, Jiaju Lu, Yuxuan Zhang, Shao Yao, Bingsheng Dong, Yuanzhe Xu, Ying Li, Yunyao Wang, Qianwen Wang, Dakuo Sun, Yuling |
| author_facet | Chen, Jiaju Lu, Yuxuan Zhang, Shao Yao, Bingsheng Dong, Yuanzhe Xu, Ying Li, Yunyao Wang, Qianwen Wang, Dakuo Sun, Yuling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09756 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children's Story-Based Learning Chen, Jiaju Lu, Yuxuan Zhang, Shao Yao, Bingsheng Dong, Yuanzhe Xu, Ying Li, Yunyao Wang, Qianwen Wang, Dakuo Sun, Yuling Computation and Language 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. |
| title | StorySparkQA: Expert-Annotated QA Pairs with Real-World Knowledge for Children's Story-Based Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.09756 |