A Generative Model Based Chatbot Using Recurrent Neural Networks

Abstract

Conversational modeling is an important task in natural language understanding and machine intelligence. It makes sense for natural language to become the primary way in which we interact with devices because that is how humans communicate with each other. Thus, the possibility of having conversations with machines would make our interaction much more smooth and human-like. The natural language techniques need to be evolved to match the level of power and sophistication that users expect from virtual assistants. Although previous approaches exist, they are often restricted to specific domains and require handcrafted rules. The obvious problem lies in their inability to answer questions for which the rules were not written. To overcome this problem, we build a generative model neural conversation system using a deep LSTM Sequence to Sequence model with an attention mechanism. Our main emphasis is to build a generative model chatbot in open domain which can have a meaningful conversation with humans. We consider Reddit conversation datasets to train the model and applied turing test on the proposed model. The proposed chatbot model is compared with Cleverbot and the results are presented.