Using chatbot for teaching arabic language syntax

Chatbots are being used in a wide variety of industries, ranging from industry to education. It is not as effective when using traditional ways of developing a chatbot system as it is when using machine learning (ML). Historically, they were created using finite-state machines, rule-based systems, and knowledge bases. Although these technologies had shortcomings, they were nonetheless employed to create chatbots. This is because natural language processing and neural network technology have simplified the task of conversational AI systems categorizing intentions and locating persons and places. Many people have asked us how we created an Arabic chatbot that understands real language, and we’d like to demonstrate how we achieved it. It is capable of responding, acting on behalf of the user, and retaining the context of a communication between two persons. We employed models such as FastText and BERT, which may be used in multiple languages concurrently. Additionally, we employed two pipeline components that we created specifically for this project.