LSTM based Ensemble Network to enhance the learning of Long-term Dependencies in Chatbot

Abstract

A chatbot is a software that can reproduce a discussion portraying a specific dimension of articulation among people and machines utilizing Natural Human Language. With the advent of AI, chatbots have developed from being minor guideline-based models to progressively modern models. A striking highlight of the current chatbot frameworks is their capacity to maintain and support explicit highlights and settings of the discussions empowering them to have human interaction in real-time surroundings. The paper presents a detailed database concerning the models utilized to deal with the learning of long haul conditions in a chatbot. The paper proposes a novel crossbreed Long Short Term Memory based Ensemble model to retain the information in specific situations. The proposed model uses a characterized number of Long Short Term Memory Networks as a significant aspect of its working as one to create the aggregate forecast class for the information inquiry and conversation. We found that both of the ensemble methods LSTM and GRU work well in different dataset environments and the ensemble technique is an effective one in chatbot applications.