Question Answering: Challenges and Solutions

This lecture delves into the challenges faced in question answering systems, such as the inability to generalize across datasets and the presence of adversarial examples. It explores the limitations of current models in handling out-of-domain distributions and the need for fine-tuning on various datasets. The lecture also discusses open-domain question answering, where systems must retrieve answers from a large document collection like Wikipedia. Different frameworks, such as the retriever-reader framework, are presented, along with the importance of dense retrieval models. The session concludes with an overview of recent advancements in generative models for question answering, showcasing the benefits of answer generation over extraction.