Continually Improving Extractive QA via Human Feedback

We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask questions, receive model-predicted answers, and provide feedback. We conduct experiments involving thousands of user interactions under diverse setups to broaden the understanding of learning from feedback over time. Our experiments show effective improvement from user feedback of extractive QA models over time across different data regimes, including significant potential for domain adaptation. * Equal contribution. 1 The term continual learning is at times used to refer to a scenario where models adapt to new tasks over time. We study improving the model continually on its original task.