Natural language processing (NLP) systems based on deep neural networks have drastically improved the ability to gain the knowledge stored in text form from humongous amount of information stored on the web or on Wikipedia using search engines and QA systems. These systems are expected to extract the precise information corresponding to a query independent of the domain. Open domain question answering (ODQA) systems have gained prominence to extract genuine answers for queries in natural language form. Although ODQA systems are famous for web search, they can be used in many applications like gaining investment insights from financial documents, addressing queries from the internal enterprise wikis, customer support for newly launched services, among other uses. Chatbots, document classification systems and other applications use Machine Reading Comprehension (MRC) which is an integral part of ODQA systems. MRC is the critical processing bottleneck in the ODQA systems to process several documents with minimal latency.