Schema induction involves creating a graph representation depicting how events unfold in a scenario. We present SAGEViz, an intuitive and modular tool that utilizes human-AI collaboration to create and update complex schema graphs efficiently, where multiple annotators (humans and models) can work simultaneously on a schema graph from any domain. The tool consists of two components: (1) a curation component powered by plug-and-play event language models to create and expand event sequences while human annotators validate and enrich the sequences to build complex hierarchical schemas, and (2) an easy-to-use visualization component to visualize schemas at varying levels of hierarchy. Using supervised and few-shot approaches, our event language models can continually predict relevant events starting from a seed event. We conduct a user study and show that users need less effort in terms of interaction steps with SAGEViz to generate schemas of better quality. We also include a video demonstrating the system.

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Empirical Methods in Natural Language Processing (EMNLP) Demo Track (2023).

@article{devare:emnlp23, title={SAGEViz: SchemA GEneration and Visualization}, author={Sugam Devare and Mahnaz Koupaee and Gautham Gunapati and Sayontan Ghosh and Sai Vallurupalli and Yash Kumar Lal and Francis Ferraro and Nathanael Chambers and Greg Durrett and Raymond Mooney and Katrin Erk and Niranjan Balasubramanian}, booktitle={Empirical Methods in Natural Language Processing (EMNLP) Demo Track}, month={December}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=128045", year={2023} }