Unsupervised Question

Unsupervised question answering (UQA) focuses on building question-answering systems without relying on large, manually labeled datasets. Current research explores diverse approaches, including leveraging multi-level summarization techniques, embedding models like BERT and its variants, and generating adversarial question-answer pairs to improve model robustness. These advancements are significant because they address the limitations of supervised methods, particularly in low-resource languages and domains with limited annotated data, paving the way for more efficient and adaptable question-answering systems.