A reinforcement-driven multiple-instance learning method for multi-task speaker attribute prediction

Standard models for predicting speaker attributes from text often fail to manage multiple, interdependent attributes simultaneously, and existing Reinforced Multiple-Instance Learning (RL–MIL) frameworks are typically limited to single-task prediction. To address this, we propose and evaluate a Reinforced, Multi-Task, Multiple-Instance Learning framework, a novel framework that enhances Reinforced Multiple-Instance Learning with Multi-Task Learning (MTL) to predict a speaker’s age, gender, and political party from congressional speeches. A central goal of our work was to investigate the optimal parameter-sharing strategy. We compared a fully shared architecture against a dynamic task clustering mechanism designed to mitigate negative transfer by adaptively grouping related tasks. Our results demonstrate that the multi-task approach significantly outperforms single-task baselines. Interestingly, the model with a fully shared representation achieved the highest macro-average F1 score of 0.668, suggesting the tasks in this dataset were sufficiently correlated to benefit from shared features without needing adaptive separation. This work contributes a more effective method for weakly supervised, multi-attribute prediction and provides crucial insights into the trade-offs between different parameter-sharing strategies in MTL.