  Question answer generation using Natural Language Processing models is
ubiquitous in the world around us. It is used in many use cases such as the
building of chat bots, suggestive prompts in google search and also as a way of
navigating information in banking mobile applications etc. It is highly
relevant because a frequently asked questions (FAQ) list can only have a finite
amount of questions but a model which can perform question answer generation
could be able to answer completely new questions that are within the scope of
the data. This helps us to be able to answer new questions accurately as long
as it is a relevant question. In commercial applications, it can be used to
increase customer satisfaction and ease of usage. However a lot of data is
generated by humans so it is susceptible to human error and this can adversely
affect the model's performance and we are investigating this through our work
