Automated Questions Answering Generation System Adopting NLP and T5
Bhimasen Moharana, Vinay Kumar Singh, Tiyas Sarkar, Dhanpratap Singh, Manik Rakhra, Vikas Pandey
Abstract
Recent advances in “Automatic Question Answer Generation” [QAG] systems have demonstrated their ability in order to increase the effectiveness of a variety of Natural Language Processing jobs. A recently developed QAG systems construct exceptional question and answer pairs using an advanced linguistics model. This method is adaptable and can generate a variety of question kinds, including wh-questions, fill in the blank questions, questions of full sentence, true false questions and multiple choice questions. Many text based datasets have been used to test the system, confirming its capacity to create exact & appropriate questions and responses for provided texts. Its applications are especially promising in contexts involving education, business and research where quick and precise text material understanding is required. Experimental findings confirm the system's usability and efficacy, indicating that it might be a significant mechanism for a variety of Natural Language Processing applications.