@inproceedings{devare-etal-2023-sageviz,
title = "{SAGEV}iz: {S}chem{A} {GE}neration and Visualization",
author = "Devare, Sugam and
Koupaee, Mahnaz and
Gunapati, Gautham and
Ghosh, Sayontan and
Vallurupalli, Sai and
Lal, Yash Kumar and
Ferraro, Francis and
Chambers, Nathanael and
Durrett, Greg and
Mooney, Raymond and
Erk, Katrin and
Balasubramanian, Niranjan",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.29",
doi = "10.18653/v1/2023.emnlp-demo.29",
pages = "328--335",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SAGEViz: SchemA GEneration and Visualization
%A Devare, Sugam
%A Koupaee, Mahnaz
%A Gunapati, Gautham
%A Ghosh, Sayontan
%A Vallurupalli, Sai
%A Lal, Yash Kumar
%A Ferraro, Francis
%A Chambers, Nathanael
%A Durrett, Greg
%A Mooney, Raymond
%A Erk, Katrin
%A Balasubramanian, Niranjan
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F devare-etal-2023-sageviz
%X 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.
%R 10.18653/v1/2023.emnlp-demo.29
%U https://aclanthology.org/2023.emnlp-demo.29
%U https://doi.org/10.18653/v1/2023.emnlp-demo.29
%P 328-335
Markdown (Informal)
[SAGEViz: SchemA GEneration and Visualization](https://aclanthology.org/2023.emnlp-demo.29) (Devare et al., EMNLP 2023)
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.