Challenge: Existing methods for event prediction are incomplete and noisy.
Approach: They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome: The proposed framework can be applied to a wide variety of newsworthy scenarios.

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RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Challenge: We present a new information extraction system that can construct temporal event graphs from news documents.
Approach: They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome: The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.
RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor (2023.emnlp-demo)

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Challenge: Existing IE tools for atomic events are limited when applied to such complex events.
Approach: They propose to use event schemas to guide the organization of complex events and to edit hierarchical graphs.
Outcome: The proposed tool outperforms existing IE visualization tools in both IE result analysis and general model improvements.
Spanless Event Annotation for Corpus-Wide Complex Event Understanding (2024.lrec-main)

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Challenge: Existing methods for annotating multilingual, multimedia data are limited by the availability of multilingual corpora for schema-based event representation.
Approach: They propose a new approach to event annotation to promote whole-corpus understanding of complex events in multilingual, multimedia data.
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Event-Guided Denoising for Multilingual Relation Learning (2020.coling-main)

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Challenge: Existing methods for general purpose relation extraction use a fixed set of predetermined relations, but research has shifted to the identification of unseen relations in any language.
Approach: They propose a method for collecting high quality relation training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost.
Outcome: The proposed method achieves comparable results to the current state-of-the-art when trained on a smaller multilingual encoder .
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.
Schema Learning Corpus: Data and Annotation Focused on Complex Events (2024.lrec-main)

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Challenge: The Schema Learning Corpus is a linguistic resource designed to support research into the structure of complex events in multilingual data.
Approach: The Schema Learning Corpus is a linguistic resource that includes large volumes of background data in English, Spanish and Russian.
Outcome: The SLC defines 100 complex events (CEs) across 12 domains and multiple documents labeled for each . multiple documents contain evidence for each step, plus labeles events and relations along with their arguments across a large tag set.
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)

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Challenge: Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system.
Approach: They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news.
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Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)

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Challenge: Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics.
Approach: They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations.
Outcome: The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science.
Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction (2025.emnlp-main)

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Challenge: Existing methods for event extraction are limited in their ability to recall nuanced or rare events.
Approach: They propose a hybrid approach that leverages a self-mixture of agents and a discriminative sequence tagger to resolve ambiguities and enhance overall event prediction quality.
Outcome: The proposed approach outperforms existing state-of-the-art methods across three benchmark datasets.
Zero-Shot Transfer Learning for Event Extraction (P18-1)

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Challenge: Existing supervised event extraction methods rely on manual annotations and features specific to each event type.
Approach: They propose a framework that maps event mentions to a specific type in an event ontology . they use existing annotations to extract event types from unstructured text data .
Outcome: The proposed framework can be applied to new unseen event types without manual annotations.

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