Domain Text Classification

Domain text classification aims to build models that accurately categorize text across different domains, even with limited labeled data in some domains. Current research heavily focuses on adversarial training methods, often within a shared-private architecture, to learn both domain-invariant and domain-specific features, and explores techniques like curriculum learning and contrastive alignment to improve model robustness and generalization. These advancements are crucial for improving the reliability and efficiency of text classification in diverse real-world applications, such as sentiment analysis and fake news detection, where data scarcity and domain shifts are common challenges.