Adult content detection still poses a great challenge for automation.
Existing classifiers primarily focus on distinguishing between erotic and
non-erotic texts. However, they often need more nuance in assessing the
potential harm. Unfortunately, the content of this nature falls beyond the
reach of generative models due to its potentially harmful nature. Ethical
restrictions prohibit large language models (LLMs) from analyzing and
classifying harmful erotics, let alone generating them to create synthetic
datasets for other neural models. In such instances where data is scarce and
challenging, a thorough analysis of the structure of such texts rather than a
large model may offer a viable solution. Especially given that harmful erotic
narratives, despite appearing similar to harmless ones, usually reveal their
harmful nature first through contextual information hidden in the non-sexual
parts of the narrative.
This paper introduces a hybrid neural and rule-based context-aware system
that leverages coreference resolution to identify harmful contextual cues in
erotic content. Collaborating with professional moderators, we compiled a
dataset and developed a classifier capable of distinguishing harmful from
non-harmful erotic content. Our hybrid model, tested on Polish text,
demonstrates a promising accuracy of 84% and a recall of 80%. Models based on
RoBERTa and Longformer without explicit usage of coreference chains achieved
significantly weaker results, underscoring the importance of coreference
resolution in detecting such nuanced content as harmful erotics. This approach
also offers the potential for enhanced visual explainability, supporting
moderators in evaluating predictions and taking necessary actions to address
harmful content.

介绍了一种混合神经和基于规则的上下文感知系统，利用共指消解来识别色情内容中的有害上下文线索，并通过与专业的审查员合作，编制了一个数据集并开发了一个能够区分有害和无害色情内容的分类器。在波兰文本上测试的混合模型展示了 84% 的准确率和 80% 的召回率，强调了共指消解在检测有害色情这类细微内容中的重要性。这种方法还提供了增强的视觉可解释性，支持审查员评估预测结果并采取必要的行动来解决有害内容。