@inproceedings{gao-etal-2023-continually,
    title = "Continually Improving Extractive {QA} via Human Feedback",
    author = "Gao, Ge  and
      Chen, Hung-Ting  and
      Artzi, Yoav  and
      Choi, Eunsol",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.emnlp-main.27/",
    doi = "10.18653/v1/2023.emnlp-main.27",
    pages = "406--423",
    abstract = "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."
}