Cross Corpus

Cross-corpus research investigates the generalizability of machine learning models across different datasets, addressing the challenge of models trained on one dataset performing poorly on others. Current research focuses on improving model robustness using techniques like contrastive learning, domain adaptation (including adversarial methods and reinforcement learning), and data augmentation, often employing transformer-based architectures or specialized neural networks like Capsule Networks. This work is crucial for building more reliable and widely applicable systems in various fields, including biomedical text mining, speech emotion recognition, and hate speech detection, ultimately enhancing the real-world impact of these technologies.