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Abstract
SQuAD 2.0 (Stanford Question Answering Dataset 2.0) is a large-scale question answering dataset that has gained significant attention in the field of natural language processing and artificial intelligence. The present paper offers an extensive evaluation of SQuAD 2.0, which encompasses a comparative study with its precursor, SQuAD 1.0, and a close examination of its answerable and unanswerable questions. Furthermore, the authors survey deep learning methodologies for addressing the unanswerable questions, the AI software that employs SQuAD 2.0, and the dataset's real-world applications in both academia and industry. The limitations of the dataset and its prospective enhancements are also discussed. Finally, the authors delve into the significance of SQuAD 2.0 in propelling question answering research and its potential impact on the development of AI.
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