A Multi-Domain and Multi-Task Generative Framework with Explicit Task and Domain Conditioning for Cross-Domain Event Extraction
2026-08-24 • Computation and Language
Computation and Language
AI summaryⓘ
The authors work on event extraction, which means finding and understanding important actions and details in text. They note that while current methods do well within one type of text, they struggle when used on different kinds of texts. To improve this, they created a single model that can learn from many types of texts and tasks by using special signals to adapt to each domain. Their approach performs well across various datasets and is flexible enough to handle different extraction setups.
event extractionevent triggersevent schemasmulti-task learningdomain adaptationlarge language modelsend-to-end extractioncross-domain generalizationdomain conditioning
Authors
Siting Liang, Omar Adjali, Daniel Sonntag
Abstract
Event extraction aims to identify event triggers, classify event types, and extract arguments to construct structured event representations. Despite strong in-domain performance, developing models that generalize robustly across domains remains challenging due to variations in contextual expressions and event schemas. Prior unified and multi-task approaches improve in-domain accuracy but exhibit limited flexibility when applied to unseen domains. Even large language model-based methods that provide full event ontologies at inference time often underperform compared to smaller, task-specific fine-tuned models. We propose a unified multi-domain and multi-task training framework that models heterogeneous event schemas within a single model. Our approach introduces domain conditioning signals, jointly with task-specific prompts, enabling dynamic adaptation to dataset-specific schemas without requiring complete event label sets at inference time. The framework supports both pipeline and end-to-end extraction settings, facilitating efficient task- and domain-level transfer. Experiments on diverse event extraction benchmarks demonstrate that our method achieves competitive performance, strong cross-domain generalization, and practical scalability, while preserving domain-specific precision.