Attention-Based Adversarial Multi-Task Comment Text Classification

Text classification and analysis play a crucial role in today's society, with multi-task learning emerging as a key research direction. In addressing the shortcomings of conventional multi-task text classification models, which struggle to extract text features effectively and often disregard the significance of input information. This paper introduces a novel method that combines attention mechanisms with adversarial multi-task learning for text classification. Focusing on text input, the attention mechanism aids in feature extraction by emphasizing important textual information. Through weighted calculations and appropriate representation, the attention mechanism improves subsequent learning tasks. Comparative analysis of four multi-task text classification models validates the effectiveness and superiority of the proposed model. The model's generalizability and accuracy in multi-task text classification are demonstrated, showcasing significant advancements over traditional models in text feature extraction and importance recognition.