Question Classification

Question classification, the task of automatically categorizing questions based on their intent or type, is crucial for improving the efficiency and effectiveness of question answering systems and related applications. Current research focuses on enhancing accuracy and robustness using various deep learning architectures, including transformer-based models like BERT and Electra, graph convolutional networks, and recurrent neural networks like LSTMs, often combined in ensemble approaches. These advancements are driving improvements in diverse fields, such as education (adaptive learning systems), healthcare (mental health support), and information retrieval, by enabling more sophisticated and context-aware question processing. Furthermore, research is actively exploring methods to address challenges like handling multilingual questions, mitigating model biases, and improving the explainability of classification processes.