SAGEViz: SchemA GEneration and Visualization

Sugam Devare, Mahnaz Koupaee, Gautham Gunapati, Sayontan Ghosh, Sai Vallurupalli, Yash Kumar Lal, Francis Ferraro, Nathanael Chambers, Greg Durrett, Raymond Mooney, Katrin Erk, Niranjan Balasubramanian


Abstract
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.
Anthology ID:
2023.emnlp-demo.29
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2023
Address:
Singapore
Editors:
Yansong Feng, Els Lefever
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
328–335
Language:
URL:
https://aclanthology.org/2023.emnlp-demo.29
DOI:
10.18653/v1/2023.emnlp-demo.29
Bibkey:
Cite (ACL):
Sugam Devare, Mahnaz Koupaee, Gautham Gunapati, Sayontan Ghosh, Sai Vallurupalli, Yash Kumar Lal, Francis Ferraro, Nathanael Chambers, Greg Durrett, Raymond Mooney, Katrin Erk, and Niranjan Balasubramanian. 2023. SAGEViz: SchemA GEneration and Visualization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 328–335, Singapore. Association for Computational Linguistics.
Cite (Informal):
SAGEViz: SchemA GEneration and Visualization (Devare et al., EMNLP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-demo.29.pdf
Video:
 https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-demo.29.mp4