Question Classification with Transfer Learning

The widespread growth of online platforms has led to an overwhelming volume of user-generated questions, many of which are redundant, poorly categorized, or lack clarity in purpose. Effective question classification has thus become a crucial task in natural language processing, especially for applications like intelligent search systems, educational forums, and conversational agents. This study explores a transfer learning-based approach for classifying questions into predefined categories by leveraging the power of RoBERTa,a robustly optimized transformer model. We rely on a preprocessed draft of the Quora Question Pairs dataset for training and evaluation. For more reliable learning, we adapt fine-tuning strategies to a subsampled subset and analyze linguistic properties and semantic embeddings. Results show higher accuracy especially among the overlapped classes or the fine grained semantic ones. Furthermore, our method demonstrates strong capability for detecting and removing duplicate questions, leading to cleaner data and more effective information retrieval. These results bolster the utility of transfer learning for tackling challenging language problems with only limited amounts of manual feature engineering. As part of future work, we are likely to generalize this model for multi-label classification, domain adaptation, and to real-time question stream analysis in continual systems.