@inproceedings{zhao-lee-2020-talk,
    title = "Talk to Papers: Bringing Neural Question Answering to Academic Search",
    author = "Zhao, Tiancheng  and
      Lee, Kyusong",
    editor = "Celikyilmaz, Asli  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.acl-demos.5/",
    doi = "10.18653/v1/2020.acl-demos.5",
    pages = "30--36",
    abstract = "We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It`s designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers. We present a large improvement over classic search engine baseline on several standard QA datasets and provide the community a collaborative data collection tool to curate the first natural language processing research QA dataset via a community effort."
}