ConvQA Model

Conversational Question Answering (ConvQA) focuses on building systems that can understand and respond to multi-turn questions, mimicking human-like dialogue. Current research emphasizes improving ConvQA's robustness by addressing challenges like implicit question-answer relationships and limited training data, often employing techniques like reinforcement learning, question reformulation, and contrastive representation learning to enhance model performance. These advancements are significant because they enable more natural and effective interactions with information sources, impacting fields such as information retrieval, customer service, and in-car systems.