A Novel Trustworthy Toxic Text Detection Method with Entropy-Oriented Invariant Representation Learning for Portuguese Community - Wenting Fan; Haoyan Song; Jun Zhang (Mathematics)
With the rapid development of digital technologies, data-driven methods have demonstrated commendable performance in the toxic text detection task. However, several challenges remain unresolved, including the inability to fully capture the nuanced semantic information embedded in text languages, the lack of robust mechanisms to handle the inherent uncertainty of text languages, and the utilization of static fusion strategies for multi-view information. To address these issues, this paper proposes a comprehensive and dynamic toxic text detection method. Specifically, we design a multi-view feature augmentation module by combining bidirectional long short-term memory and BERT as a dual-stream framework. This module captures a more holistic representation of semantic information by learning both local and global features of texts. Next, we introduce an entropy-oriented invariant learning module by minimizing the conditional entropy between view-specific representations to align consistent information, thereby enhancing the representation generalization. Meanwhile, we devise a trustworthy text recognition module by defining the Dirichlet function to model uncertainty estimation of text prediction. And then, we perform the evidence-based information fusion strategy to dynamically aggregate decision information between views with the help of the Dirichlet distribution. Through these components, the proposed method aims to overcome the limitations of traditional methods and provide a more accurate and reliable solution for toxic language detection. Finally, extensive experiments on the two real-world datasets show the effectiveness and superiority of the proposed method in comparison with seven methods.