Integrate machine learning and deep learning techniques, including transformer-based NLP architectures such as BERT and GPT, in order to improve dialogue management, contextual understanding, and response generation capabilities of the chatbot system.

Abstract

The development of intelligent chatbot systems using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) has become an important research area for improving human-computer interaction. Traditional chatbot systems often face limitations such as weak contextual understanding, poor dialogue continuity, and inaccurate response generation. To address these challenges, this research proposes a context-aware chatbot system integrating machine learning, deep learning, and transformer-based NLP architectures such as BERT and GPT to enhance dialogue management, contextual understanding, and conversational response generation. The proposed framework combines contextual memory management, sentiment analysis, and Retrieval-Augmented Generation (RAG) to improve semantic interpretation and conversational adaptability. BERT is utilized for bidirectional contextual understanding and intent recognition, while GPT is employed for dynamic and coherent response generation. Experimental evaluation is conducted using multiple performance metrics including accuracy, precision, recall, F1-score, perplexity, semantic similarity, contextual relevance score, dialogue success rate, BLEU score, and ROUGE score. The proposed Hybrid BERT-GPT model achieved superior performance with 96.48% accuracy, 95.72% precision, 95.30% recall, and 95.51% F1-score, along with improved contextual relevance and dialogue success rates. The results demonstrate that integrating transformer-based NLP models significantly improves conversational intelligence, contextual reasoning, emotional adaptability, and user satisfaction in chatbot systems.