Continual Text Classification

Continual text classification focuses on training machine learning models to learn new text classification tasks sequentially without forgetting previously acquired knowledge, a challenge known as catastrophic forgetting. Current research explores various strategies, including memory-based approaches (e.g., replaying past data), architectural modifications (e.g., using specialized layers or gating mechanisms), and regularization techniques to mitigate forgetting and improve performance on both new and old tasks. This field is crucial for developing robust and adaptable AI systems capable of handling real-world scenarios where data streams continuously and task definitions evolve over time, impacting applications ranging from medical diagnosis to sentiment analysis.