For robust ML and NLP model, training the chatbot dataset with correct big data leads to desirable results. Chatbots are artificial intelligence software that simulates conversations with the user in natural language across various social interaction channels such as messaging applications, websites, and mobile applications or through the telephone. The global chatbot market size is forecasted to grow from US$2.6 billion in 2019 to US$ 9.4 billion by 2024 at a CAGR of 29.7% during the forecast period. The chatbot datasets are trained for machine learning and natural language processing models. In retrospect, NLP helps chatbots training.

An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention. However, the primary bottleneck in chatbot development is obtaining realistic, task-oriented dialog data to train these machine learning-based systems. We've put together the ultimate list of the best conversational datasets to train a chatbot, broken down into question-answer data, customer support data, dialogue data and multilingual data. Question-Answer Dataset: This corpus includes Wikipedia articles, manually-generated factoid questions from them, and manually-generated answers to these questions, for use in academic research. The WikiQA Corpus: A publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.