Contrastive Learning for Implicit Hate Detection
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Solution Overview
Problem
Deep learning-based hate expression detection models face generalization issues due to learning spurious correlations, leading to poor performance when evaluated with different datasets, especially in detecting implicit hate expressions with superficial signals.
Innovation Solution
A contrastive learning method using a combination of cross entropy and contrastive loss functions, where semantically similar but superficially different texts are used as positive samples for training, along with texts representing implications for hate expressions, to improve model generalization and detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If deep learning-based detection models are trained using cross entropy loss function, then training process is simple, but generalization performance deteriorates due to learning spurious correlations
Solution Approach 1:
The patent changes the loss function parameter from standard cross entropy to contrastive loss, which fundamentally alters how the model learns from data. This parameter change enables the model to distinguish between superficial similarities and meaningful relationships, thereby improving generalization performance while maintaining training feasibility
Solution Approach 2:
The patent introduces an intermediary contrastive loss function that mediates between the input text and the detection output. This intermediary mechanism helps the model learn robust features by comparing positive and negative samples, effectively preventing the model from learning spurious correlations while maintaining training simplicity
2Measurement precision
If models learn superficial patterns for hate expression detection, then detection accuracy on training data improves, but performance on different datasets deteriorates
Solution Approach 1:
The patent implements feedback through the contrastive loss function, which continuously compares predicted probabilities against actual labels and adjusts the model parameters accordingly. This feedback mechanism ensures the model learns meaningful patterns rather than superficial correlations, improving both training accuracy and cross-dataset performance
Solution Approach 2:
The patent performs preliminary action by pre-processing the training data to create positive and negative sample pairs before training. This preliminary organization of data into contrastive pairs enables the model to learn robust features from the outset, improving generalization to different datasets without requiring additional data processing during deployment
3Reliability
If contrastive learning with implications is applied, then generalization performance improves, but training complexity increases due to positive sample generation
Solution Approach 1:
The patent applies self-service by enabling the model to generate its own positive samples through implication extraction during training. The model learns to identify and create meaningful positive sample pairs automatically, reducing the need for external data curation and simplifying the overall training process while maintaining high generalization performance
Solution Approach 2:
The patent achieves universality by designing a single training framework that handles multiple tasks: hate expression detection, positive sample generation, and implication extraction. This multi-functional approach consolidates what would otherwise be separate processes into one unified training pipeline, improving generalization without proportionally increasing complexity
Data Source
AI summary
The contrastive learning method based on implications for detecting implicit hate expression, an apparatus, and a computer program for performing the same according to the exemplary embodiment of the present disclosure perform the contrastive learning based on implications of implicit hate expression to detect the implicit hate expression to train a network model having a higher generalization performance for the implicit hate expression.