{"type":"ScholarlyArticle","title":"A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework","authors":[{"name":"Siting Liang","slug":"siting-liang","given":"Siting","family":"Liang","affiliation":""},{"name":"Omar Adjali","slug":"omar-adjali","given":"Omar","family":"Adjali","affiliation":""},{"name":"Omair Shahzad Bhatti","slug":"omair-shahzad-bhatti","given":"Omair Shahzad","family":"Bhatti","affiliation":""},{"name":"Daniel Sonntag","slug":"daniel-sonntag","given":"Daniel","family":"Sonntag","affiliation":""}],"abstract":"Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative, sequence-to-sequence framework that performs all event extraction subtasks jointly and supports both end-to-end and pipeline configurations. We fine-tune pre-trained language models on multiple event datasets across diverse domains, enabling a single model to retain domain-specific semantics while generalizing over large, evolving label spaces. Cross-domain experiments show strong, robust performance across datasets, demonstrating a scalable solution for real-world event extraction. We demonstrate these capabilities through a web-based application tailored for researchers and practitioners. The platform supports inspection of different configurations and facilitates cross-domain comparisons.","doi":"10.24963/ijcai.2026/977","url":"https://ijcai.org/proceedings/2026/977","pdf":"https://ijcai.org/proceedings/2026/0977.pdf","markdown":"https://ijcai.org/proceedings/2026/977.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/977","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Demo Track","track":"Demo Track","number":977,"pages":{"first":8449,"last":8452},"keywords":["AI: Humans and AI","AI: Machine Learning","AI: Natural Language Processing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}