An intelligent chatbot design and implementation model using long short-term memory with recurrent neural networks and attention mechanism

Abstract

Assistant Conversational Agents (chatbots) are becoming increasingly complex and sophisticated since they are enabled to understand and respond to human language. Chatbots have varying organizational functions, from customer support to personal assistance, and have dominated the digital market as they are efficient and effective. Chatbots can collect valuable information from customers through interaction which can result in a better customer experience, but an inappropriate or inadequate response can also lead to the loss of customers. Therefore, the chatbots must be intelligent enough to provide the most efficient and effective response to positively impact the customers and add value to the business and organization. Over the years, several streams of research have been conducted, and multiple chatbots have been created, but they have their limitation, which has been discussed in this article. We suggest a method to upgrade the performance of the chatbots so that they can respond to the user query with better accuracy. The proposed model shows the implementation of a chatbot using Long Short-Term Memory (LSTM), attention mechanism, Bag of Words (BOW), and beam search decoding. The sequence-to-sequence (Seq2Seq) architecture with an LSTM encoder and decoder has been used. The dialog dataset is preferred to train and test the model, and the BleuN algorithm is used to determine the chatbot’s accuracy.