TOXCL: A Unified Artificial Intelligence Framework for the Detection and Explanation of Implicit Toxic Speech
Addressing the spread of toxic speech on social media, researchers have developed ToXCL, an AI framework designed to detect and explain implicit toxicity. This innovative approach aims to protect individuals and marginalized groups from harmful content.
Practical Solutions and Value
ToXCL utilizes a multi-module approach, including the Target Group Generator, Encoder-Decoder Model, and Conditional Decoding Constraint, to effectively identify and explain toxic posts. By incorporating a strong Teacher Classifier and knowledge distillation technique, the framework enhances its ability to detect veiled toxicity and generate accurate explanations.
Results from two implicit toxicity benchmarks demonstrate that ToXCL outperformed existing models, showcasing improved correctness, fluency, and reduced harmfulness in its outputs. While there is room for improvement, this AI framework marks a significant advancement in identifying and articulating the impacts of veiled hatred.
For companies looking to leverage AI for automation and improved customer engagement, ToXCL represents a powerful tool to combat toxic speech and protect online communities. By embracing AI solutions, businesses can redefine their workflows, identify automation opportunities, and enhance customer interactions.
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