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.

Similar Papers

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.
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%.
Complex Event Schema Induction with Knowledge-Enriched Diffusion Model (2023.findings-emnlp)

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Challenge: Existing studies on event schema induction have been hindered by errors and data quality issues.
Approach: They propose a knowledge-enriched discrete diffusion model that distills event scenario knowledge from LLMs.
Outcome: The proposed model achieves outstanding performance across evaluation metrics.
SAGEViz: SchemA GEneration and Visualization (2023.emnlp-demo)

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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.
Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)

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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.
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.
Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)

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Challenge: Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored.
Approach: They propose to use large-scale pre-trained language models to generate an event-level temporal graph from a document using existing IE/NLP tools.
Outcome: The proposed method outperforms the closest existing method on several metrics on a hand-labeled, out-of-domain corpus.
Mining Logical Event Schemas From Pre-Trained Language Models (2022.acl-srw)

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Challenge: a pre-trained language model is induced into acting as a distribution over stories, a new system is proposed . NESL is a neural event schema learning system that combines large language models, FrameNet parsing, and simple behavioral schemas to bootstrap the learning process.
Approach: They propose a neural event schema learning system that bootstraps the learning process by parsing pre-trained language models into simple behavioral schemas.
Outcome: The proposed system combines large language models, a powerful logical representation of language, and simple behavioral schemas to bootstrap the learning process.
A Graph Enhanced BERT Model for Event Prediction (2022.findings-acl)

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Challenge: Existing methods to predict subsequent events use sparsity of event graph to improve performance.
Approach: They propose to automatically build event graph using a BERT model by adding a structured variable to the model to learn to predict event connections.
Outcome: The proposed model outperforms state-of-the-art models on two event prediction tasks.
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)

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Challenge: Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way.
Approach: They examine whether syntactic and semantic graph representations can complement and improve neural language modeling.
Outcome: The proposed model outperforms pretrained models on English WSJ in perplexity and other metrics.

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