Chatbots are replacing a number of the roles that were traditionally performed by human workers, like online
customer service agents and educators. From the initial stage of rule-based chatbots to the time of rapid development in AI, the
performance of chatbots keeps improving.
The aim of this research is to develop a chatbot for general conversation using Cornell movie corpus dataset, a dataset of more
than 600 movies containing thousands of conversations between lots of characters. Moreover, the model can be used to train
different datasets to create chatbots in any domain such as chatbots for movie buffs, weather forecasting, taking online
appointment with doctor as and more. It deals with building of a super powerful chatbot but by implementing a state of the art
and Deep Natural Language processing model. The seq2seq model will be implemented with one of the best API to build deep
learning applications or artificial intelligence, which will be tensor flow and generate a chatbot for general conversation.
