DESIGNING EFFECTIVE CHATBOT SYSTEM USING GRU WITH BEAM SEARCH

Abstract

Artificial Intelligence (AI) based Chatbot is a moderately new technology in the world. AI and Natural Language Processing (NLP) empowers a Chatbot to converse like a human being. Chatbots have become popular recently as they diminish human efforts by automating various tasks. AI-based Chatbot learns from the previous discussion and generates an appropriate response or action for the input given by the user. In the proposed research work we designed AI-based Chatbot system using the Sequence to Sequence (Seq2Seq) model. This system uses a Gated Recurrent Unit (GRU) for encoder and decoder. In the proposed model the GRU encoder accepts a query from the user. The GRU encoder uses an attention mechanism to consider only relevant information and convert it into the context vector form. A context vector is another input to the GRU decoder. The GRU decoder generates a response using the Beam search algorithm. The research work uses Cornell Movie Dialogue Corpus to train the proposed interactive Chatbot system. It is observed that the proposed model with the combination of GRU and Beam search gives better accuracy with the minimum loss for testing data. These experimental results are better than existing approaches that use LSTM Seq2Seq models to train Chatbot systems.

Keywords

• chatbot
• deep learning
• rnn
• gru
• attention mechanism