The paper introduces a scalable cross‑domain event extraction system built on a unified generative sequence‑to‑sequence framework. It jointly handles event detection and argument extraction, allowing both pipeline and end‑to‑end configurations. By fine‑tuning pretrained language models on multiple event datasets from diverse domains, the system retains domain‑specific semantics while generalizing across large, evolving label spaces, and offers a web‑based application for researchers to upload documents, extract events, visualize triggers and arguments, and compare configurations.
By Siting Liang, Omar Adjali, Omair Shahzad Bhatti, Daniel Sonntag
arXiv:2610.19021v1 Announce Type: new
Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, L...
By Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi, Nikita Dvornik, Alexander Pashevich
The paper introduces a unified multi-domain and multi-task generative framework for event extraction that incorporates explicit domain conditioning signals and task-specific prompts. This design allows a single model to adapt dynamically to different event schemas without needing full event label sets during inference, supporting both pipeline and end-to-end extraction. Experiments on various benchmarks show competitive performance, strong cross-domain generalization, and practical scalability while maintaining domain-specific precision.
By Siting Liang, Omar Adjali, Daniel Sonntag
EventVL introduces the first generative event-based multimodal large language model (MLLM) designed for explicit semantic understanding of event streams. The framework leverages a newly annotated dataset of nearly 1.4 million event–image/video–text pairs and incorporates an Event Spatiotemporal Representation to capture comprehensive event information, along with Dynamic Semantic Alignment to refine sparse semantic spaces. Experiments demonstrate that EventVL outperforms existing MLLM baselines in event captioning and scene description generation tasks, advancing the field of event vision.
By Pengteng Li, Yunfan Lu, Pinghao Song, Wuyang Li, Huizai Yao, Hui Xiong
arXiv:2610.17113v1 Announce Type: cross
Abstract: Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remai...
By Yang Wang, Hanlin Xiao, Yanan Ma, Chenghao Xiao, Chenghua Lin
Hyper-RED introduces a scalable image-to-event pretraining framework that transfers high‑order semantic structures via hypergraphs, avoiding rigid pixel‑wise alignment. By constructing image, event, and cross‑modal hypergraphs and applying a hypergraph relational distillation loss, the method preserves local relational consistency and event‑specific characteristics while inheriting image‑derived semantic organization. Experiments across five event datasets show consistent scaling from ViT‑S to ViT‑L and state‑of‑the‑art performance.
By Meisen Wang, Zhiqiang Tian, Wei Bao, Chengjie Wang, Shaoyi Du, Siqi Li