Automatic Question Answer Generation using T5 and NLP

Automatic Question Answer Generation (QAG) systems have received significant attention in recent years due to their potential to improve the efficiency of various natural language processing tasks. A QAG system is proposed that utilizes a state-of-the-art language model to generate high-quality question-answer pairs. The system supports various types of questions, including Wh-questions, fill-in-the-blank, full-sentence questions, multiple-choice questions, and true-false questions. System is developed and tested on a diverse range of text-based datasets, and the results show that it can generate accurate and relevant questions and answers for a given piece of text. The QAG system has significant potential for use in educational, industrial, and research settings where quick and efficient comprehension of text is crucial. The system’s usability and effectiveness are demonstrated through experiments, and it is believed that it has the potential to be a valuable tool for a wide range of natural language processing tasks.