ConvQA Face Challenge

ConvQA (Conversational Question Answering) research focuses on building systems that can engage in multi-turn, context-aware dialogues to answer complex questions. Current efforts concentrate on improving model robustness to noisy or implicit questions through techniques like query reformulation, reinforcement learning, and leveraging large language models to generate synthetic training data or improve answer selection. These advancements aim to address limitations in existing ConvQA systems, ultimately leading to more accurate and natural-sounding conversational AI agents with applications in diverse fields like digital assistants and information retrieval.