Natural Language Generation Using Markov Chains for Chatbot

This paper presents a mini project on Natural Language Generation (NLG) using Markov Chains for chatbot development. The problem addressed is the challenge of generating coherent and contextually relevant dialogue in chatbots, a fundamental component of conversational AI. While many advanced models such as transformers and recurrent neural networks (RNNs) excel in generating context-aware responses, simpler methods like Markov Chains remain a popular approach for basic conversational systems. The motivation behind this project is to explore the feasibility of using Markov Chains in text generation, which offers a simple, probabilistic approach to model word sequences. The background of this work lies in the concept of Markov Chains, where the generation of each word depends on the previous word, creating a chain of predictions based on learned word pairs. This method is widely used in natural language processing for tasks like text prediction and sentence generation. However, it faces limitations in handling long-term dependencies and maintaining context over extended conversations. we designed a chatbot using Markov Chains to generate responses based on a dataset of dialogues. The process involved preprocessing the text, building a Markov Chain model from the dataset, and implementing a simple chatbot interface. The results showed that while the chatbot could generate random, contextually linked responses, it often lacked logical coherence, demonstrating the simplicity and limitations of the Markov Chain approach.