Develop a multi-turn dialogue system using state-of-the-art machine learning techniques for conversational AI applications.
Design and implement natural language understanding (NLU) and natural language generation (NLG) components for effectively processing user inputs and generating appropriate responses.
Evaluate the performance of the dialogue system through user studies and metrics such as accuracy, fluency, and engagement.
Explore and incorporate advanced techniques such as reinforcement learning and attention mechanisms to enhance the system's conversational abilities.
Research and review existing literature on multi-turn dialogue systems and conversational AI.
Design and implement models for NLU and NLG using machine learning frameworks such as TensorFlow or PyTorch.
Collect and preprocess a suitable dataset for training and evaluation.
Train and fine-tune the dialogue system model with the collected dataset.
Conduct user studies to assess the system's performance and gather feedback for further improvements.
