Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective setting

Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. For hate comment classification, an agent must balance maximizing the F1-score while minimizing False Positives (FP) to enhance precision and reduce misclassifications. However, such multi-objective optimization introduces uncertainties in decision-making. To address this, we propose a Bayesian Q-Learning framework with a convolutional neural network policy. The policy outputs action logits, integrated with Q-value estimates sampled via Thompson Sampling from a Gaussian posterior. Our reward function combines F1-score (objective 1) and a penalty for misclassification (objective 2) to optimize learning. To validate our framework, firstly we show that our framework classifies the hate-comments comparatively better than other baselines by scoring an F1-score of 83%, 93%, 77% and 71% in English-Tamil, English, Kannada and Malayalam datasets for detecting homophobic and transphobic comments respectively. Secondly, we demonstrate that the variance of Q-value estimates in our Bayesian posterior decreases significantly over time, indicating that the agent has learned an optimal policy that effectively balances the competing objectives. This finding is further supported by statistical t-tests conducted across all datasets, which confirm the significance of the observed variance reduction. Additionally, we observe our agent’s multi-objective optimization path in 3D space, which shows its ability to balance reward (F1-score) and regret. Furthermore, we compare the action selection between our Bayesian approach and non-Bayesian action clustering using K-Means algorithms, where our analysis highlights coherent clustering which indicates structure exploration, while non-Bayesian approach shows premature convergence to suboptimal policies.