| Challenge: | Detecting multiple events from natural language text is a challenge because of the following problems: a) Sentence-level contextual representation and document-level information aggregation are not enough to detect event triggers. |
| Approach: | They propose a multi-layer bidirectional network to capture document-level association of events and semantic information simultaneously. |
| Outcome: | The proposed approach improves performance over the current state-of-the-art approach. |
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Document-level Event Extraction via Parallel Prediction Networks (2021.acl-long)
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| Challenge: | Document-level event extraction (DEE) is indispensable when events are described throughout a document. |
| Approach: | They propose a document-level event extraction model that can extract structured events from a text in parallel. |
| Outcome: | The proposed model outperforms current state-of-the-art methods on a document-level event extraction task. |
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (D18-1)
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| Challenge: | Existing approaches to ACE event detection treat multiple events in one sentence as independent ones and recognize them separately. |
| Approach: | They propose a hierarchical and bias tagging network framework to detect multiple events in one sentence collectively and a gated multi-level attention mechanism to automatically extract and fuse the sentence-level and document-level information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a 2005 ACE dataset. |
Event Detection as Graph Parsing (2021.findings-acl)
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| Challenge: | Existing approaches to event detection focus on using syntactic dependency structures or external knowledge to boost the performance. |
| Approach: | They propose a graph parsing problem that explicitly models multiple event correlations and utilizes rich information conveyed by event type and subtype. |
| Outcome: | The proposed model outperforms existing models on the public ACE2005 dataset by 4.2% on the dataset. |
Exploring Sentence Community for Document-Level Event Extraction (2021.findings-emnlp)
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| Challenge: | Existing approaches to document-level event extraction neglect the complex logic structures in long texts. |
| Approach: | They propose a framework that exploits the relationship between sentences to extract multiple events by sentence community detection using graph attention networks. |
| Outcome: | The proposed framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset. |
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (D18-1)
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| Challenge: | Event extraction is of practical utility in natural language processing . it is common that multiple events exist in the same sentence, causing difficulties in extracting them . |
| Approach: | They propose a framework to jointly extract multiple event triggers and arguments . they introduce syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. |
| Outcome: | The proposed framework achieves competitive results compared with state-of-the-art methods. |
Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
| Outcome: | The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction. |
Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation (2024.lrec-main)
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| Challenge: | Document-level Event Extraction (DEE) is a vital task in NLP . current approaches overlook intricate relationships among events and subtle correlations among arguments within a document . |
| Approach: | They propose a document-level event extraction tool that integrates event relationships and argument correlation graphs to model the relationship among events. |
| Outcome: | The proposed network outperforms existing models and large language models in terms of F1-score across two benchmark datasets. |
Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020.aacl-main)
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| Challenge: | Existing approaches for event extraction focus on sentence-level event extraction, but they lack a broader view of the document context. |
| Approach: | They build graphs with candidate event filler extractions enriched by sentential embeddings as nodes and use graph attention networks to identify event regions in a document and aggregate event information. |
| Outcome: | The proposed method performs well on two languages and shows that it is faster than previous methods. |
Cross-media Structured Common Space for Multimedia Event Extraction (2020.acl-main)
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| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
| Outcome: | The proposed method achieves 4.0% and 9.8% absolute gains on text event argument role labeling and visual event extraction. |
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. |