@inproceedings{ravfogel-etal-2021-neural,
    title = "Neural Extractive Search",
    author = "Ravfogel, Shauli  and
      Taub-Tabib, Hillel  and
      Goldberg, Yoav",
    editor = "Ji, Heng  and
      Park, Jong C.  and
      Xia, Rui",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.acl-demo.25/",
    doi = "10.18653/v1/2021.acl-demo.25",
    pages = "210--217",
    abstract = "Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called {\textquotedblleft}extractive search{\textquotedblright}, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at \url{https://spike.neural-sim.apps.allenai.org/} and a video demonstration is available at \url{https://vimeo.com/559586687}."
}