@inproceedings{elgohary-etal-2018-dataset,
    title = "A dataset and baselines for sequential open-domain question answering",
    author = "Elgohary, Ahmed  and
      Zhao, Chen  and
      Boyd-Graber, Jordan",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D18-1134/",
    doi = "10.18653/v1/D18-1134",
    pages = "1077--1083",
    abstract = "Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at \url{http://sequential.qanta.org}."
}