Question Answering (QA) system is an automated approach to retrieve correct responses to the questions asked by human in natural language. Reading comprehension (RC)in contrast to information retrieval, requires integrating information and reasoning about events, entities, and their relations across a full document. Immense progress has been made in the recent years for this task, since the advent of deep learning and use of sequence to sequence models for NLP. This thesis deals with two complex tasks in Question Answering with their own inherent challenges: Multi Task Learning for Narrative Question Answering, which involves developing models to deal with the complexity of the domain of stories, movie scripts and human written answers, and second task is to develop novel ways of incorporating common sense knowledge from external knowledge bases for automated question answering. The models developed for these tasks help to advance research in the area of question answering and highlights some of the shortcomings of the methods proposed in literature
