Automatic question-answer pair generation using Deep Learning

Automatic question-answer pair generation is gaining importance because of the time being saved when compared to manual creation of questions. It not only saves time but also reduces the effort involved in the manual question generation process. It is useful in many fields such as school assignments, law practicing, self-assessments and many more. Our main objective is to create wh-type questions from a paragraph and find its accurate answer as well. This research work introduces a Question Answer Generation (QAG) system by using a deep learning approach for combining Answer Extraction (AE), Question Generation (QG) and Question Answering (QA) models. This research work aims to use a pre-trained language model - Bidirectional Encoder Representation from Transformers (BERT) and fine-tune it as per the proposed research objective.