Prediction Uncertainty Estimation for Hate Speech Classification
This addresses the need for reliable hate speech detection to protect minority groups while preserving free speech, though it is incremental as it adapts existing uncertainty estimation techniques to a specific domain.
The paper tackles the problem of hate speech detection by proposing a method to estimate prediction uncertainty in text classification, using Monte Carlo dropout regularization to mimic Bayesian inference and evaluating it with various text embeddings.
As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large communities, there is a pressing need for hate speech detection and filtering. However, automatic approaches shall not jeopardize free speech, so they shall accompany their decisions with explanations and assessment of uncertainty. Thus, there is a need for predictive machine learning models that not only detect hate speech but also help users understand when texts cross the line and become unacceptable. The reliability of predictions is usually not addressed in text classification. We fill this gap by proposing the adaptation of deep neural networks that can efficiently estimate prediction uncertainty. To reliably detect hate speech, we use Monte Carlo dropout regularization, which mimics Bayesian inference within neural networks. We evaluate our approach using different text embedding methods. We visualize the reliability of results with a novel technique that aids in understanding the classification reliability and errors.