Conversational AI Chatbot Based on Encoder-Decoder Architectures with Attention Mechanism
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Conversational AI Chatbot development using Artificial Intelligence and Machine Learning technique is an interesting problem of Natural Language Processing. In many research and development projects scientists are using AI, Machine Learning algorithms and Natural Language Processing techniques for developing Conversational AI Chatbot. The research and development of automated help desk and customer services through these conversation agents are still under progress and experimentation. Conversational AI Chatbot is mostly deployed by financially organizations like the bank, credit card companies, businesses like online retail stores and startups. Virtual agents are adopted by businesses ranging from very small start-ups to large corporations. There are many AI Chabot development frameworks available in the market both program-based and interface based. But they lack the accuracy and flexibility in developing real dialogues. Among these popular intelligent personal assistants are Amazon’s Alexa, Microsoft’s Cortana and Google’s Google Assistant. The functioning of these agents is limited, and retrieval based agent which are not aimed at holding conversations that emulate real human interaction. Among current chatbots, many are developed using rule-based techniques, simple machine learning algorithms or retrieval based techniques which do not generate good results. In this paper, we have developed a Conversational AI Chatbot using modern-day techniques. For developing Conversational AI Chatbot, We have implemented encoder-decoder attention mechanism architecture. This encoder-decoder is using Recurrent Neural Network with LSTM (LongShort-Term-Memory) cells.
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