Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic Discourse
This addresses the need for robust content moderation in non-English contexts like Polish, though it is incremental as it focuses on a specific language and dataset creation.
The authors tackled the problem of detecting erotic content in Polish by creating the forePLay dataset with over 24k annotated sentences, and found that specialized Polish language models outperformed multilingual alternatives, with transformers excelling in imbalanced categories.
The surge in online content has created an urgent demand for robust detection systems, especially in non-English contexts where current tools demonstrate significant limitations. We present forePLay, a novel Polish language dataset for erotic content detection, featuring over 24k annotated sentences with a multidimensional taxonomy encompassing ambiguity, violence, and social unacceptability dimensions. Our comprehensive evaluation demonstrates that specialized Polish language models achieve superior performance compared to multilingual alternatives, with transformer-based architectures showing particular strength in handling imbalanced categories. The dataset and accompanying analysis establish essential frameworks for developing linguistically-aware content moderation systems, while highlighting critical considerations for extending such capabilities to morphologically complex languages.