Sai Vallurupalli


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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
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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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POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
Sai Vallurupalli | Sayontan Ghosh | Katrin Erk | Niranjan Balasubramanian | Francis Ferraro
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Knowledge about outcomes is critical for complex event understanding but is hard to acquire.We show that by pre-identifying a participant in a complex event, crowdworkers are ableto (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground theoutcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96weighted Fleiss Kappa). Our dataset, POQUe (Participant Outcome Questions), enables theexploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant’s influence over the event culmination.