Zhang, TianyiTham, IsaacHou, ZhaoyiRen, JiaxuanZhou, LeonXu, HainiuZhang, LiMartin, Lara J.Dror, RotemLi, ShaJi, HengPalmer, MarthaBrown, Susan WindischSuchocki, ReeceCallison-Burch, Chris2024-03-042024-03-042023-07Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, and Chris Callison-Burch. 2023. Human-in-the-loop Schema Induction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 1–10, Toronto, Canada. Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-demo.1https://doi.org/10.18653/v1/2023.acl-demo.1http://hdl.handle.net/11603/31769Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, July 2023, Toronto, CanadaSchema 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.10 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.CC BY 4.0 DEED Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Text