Alicia Y. Tsai


2024

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PG-Story: Taxonomy, Dataset, and Evaluation for Ensuring Child-Safe Content for Story Generation
Alicia Y. Tsai | Shereen Oraby | Anjali Narayan-Chen | Alessandra Cervone | Spandana Gella | Apurv Verma | Tagyoung Chung | Jing Huang | Nanyun Peng
Proceedings of the Third Workshop on NLP for Positive Impact

Creating children’s stories through text generation is a creative task that requires stories to be both entertaining and suitable for young audiences. However, since current story generation systems often rely on pre-trained language models fine-tuned with limited story data, they may not always prioritize child-friendliness. This can lead to the unintended generation of stories containing problematic elements such as violence, profanity, and biases. Regrettably, despite the significance of these concerns, there is a lack of clear guidelines and benchmark datasets for ensuring content safety for children. In this paper, we introduce a taxonomy specifically tailored to assess content safety in text, with a strong emphasis on children’s well-being. We present PG-Story, a dataset that includes detailed annotations for both sentence-level and discourse-level safety. We demonstrate the potential of identifying unsafe content through self-diagnosis and employing controllable generation techniques during the decoding phase to minimize unsafe elements in generated stories.