@inproceedings{zhang-etal-2023-human,
    title = "Human-in-the-loop Schema Induction",
    author = "Zhang, Tianyi  and
      Tham, Isaac  and
      Hou, Zhaoyi  and
      Ren, Jiaxuan  and
      Zhou, Leon  and
      Xu, Hainiu  and
      Zhang, Li  and
      Martin, Lara  and
      Dror, Rotem  and
      Li, Sha  and
      Ji, Heng  and
      Palmer, Martha  and
      Brown, Susan Windisch  and
      Suchocki, Reece  and
      Callison-Burch, Chris",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.1",
    doi = "10.18653/v1/2023.acl-demo.1",
    pages = "1--10",
    abstract = "Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction (IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.",
}