| Challenge: | Experimental results show that a new method for learning event schemas from historical events is effective. |
| Approach: | They propose a new event schema induction framework which captures global dependencies among nodes in event graphs. |
| Outcome: | Experimental results show that the proposed model can learn event schemas with global consistency. |
Similar Papers
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction (2021.emnlp-main)
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| Challenge: | Event schemas encode knowledge of stereotypical structures of events and their connections . previous work on event schema induction focuses on atomic events or linear temporal sequences . |
| Approach: | They propose a Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. |
| Outcome: | The proposed model outperforms existing models on HITS@1 by 17.8%. |
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification (2023.acl-long)
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| Challenge: | Recent methods for event schema induction use information extraction systems to construct event graph instances from documents . compared to the previous state-of-the-art closed-domain schema inducing model, human assessors were able to cover 10% more events when translating the schemas into coherent stories . |
| Approach: | They propose to treat event schemas as commonsense knowledge that can be derived from large language models. |
| Outcome: | The proposed method simplifies the schema induction process and improves readability. |
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020.emnlp-main)
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| Challenge: | Existing methods to automate event extraction focus on uncertainty, re-occurring events and multiple hypotheses. |
| Approach: | They propose a new Event Graph Schema where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. |
| Outcome: | The proposed model is highly effective at inducing salient and coherent schemas. |
Schema-based Data Augmentation for Event Extraction (2024.lrec-main)
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| Challenge: | Existing data augmentation methods rely on language models to train event extraction models. |
| Approach: | They propose a schema-based data augmentation method that utilizes event schemas to guide the data generation process. |
| Outcome: | The proposed method produces high-quality generated data and significantly improves model performance. |
Learning Event Graph Knowledge for Abductive Reasoning (2021.acl-long)
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| Challenge: | Existing models for abductive reasoning based on formal logic lack commonsense knowledge and effective reasoning mechanism. |
| Approach: | They propose a narrative text-based abductive reasoning task NLI with a latent variable to capture commonsense knowledge from event graph for guiding the abductive reasoning task. |
| Outcome: | The proposed model outperforms baseline methods on the abductive reasoning task. |
Semi-supervised New Event Type Induction and Event Detection (2020.emnlp-main)
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| Challenge: | Existing event extraction studies assume a set of event types and corresponding annotations are given, which could be expensive. |
| Approach: | They propose a semi-supervised task of event type induction to learn seen and unseen types . they use seen type annotations to optimize the process and enforce the reconstruction . |
| Outcome: | The proposed method achieves state-of-the-art on supervised event detection and discovers high-quality new types. |
SAGEViz: SchemA GEneration and Visualization (2023.emnlp-demo)
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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
| Challenge: | Schema induction involves creating a graph representation depicting how events unfold . supervised and few-shot approaches are not scalable and time-consuming . |
| Approach: | They propose a tool that utilizes human-AI collaboration to create and update complex schema graphs efficiently. |
| Outcome: | The proposed tool can generate schemas of better quality and be used by users in a variety of domains. |
Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)
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| Challenge: | Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure . |
| Approach: | They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events . |
| Outcome: | The proposed model improves on the ACE05, RAMS, and WikiEvents datasets. |
Schema-adaptable Knowledge Graph Construction (2023.findings-emnlp)
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| Challenge: | Existing Knowledge Graph Construction (KGC) tasks rely on static information extraction with a closed set of pre-defined schemas. |
| Approach: | They propose a static knowledge Graph Construction task that extracts entity, relation, and event based on dynamically changing schema graph without retraining. |
| Outcome: | The proposed system outperforms existing methods but still has room for improvement . it can extract entity, relation, and event based on dynamically changing schema graph without re-training . |
Zero-Shot On-the-Fly Event Schema Induction (2023.findings-eacl)
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| Challenge: | a new approach to event processing uses large language models to generate source documents that can be curated without manual data collection. |
| Approach: | They propose a framework that generates a graphical representation of events in documents . they show that the model is more complete than previous supervised methods . |
| Outcome: | The proposed model is more complete than human-curated schemas in most scenarios. |