When Tasks Share Structure: A Comparative Study of Training Strategies for Generative Event Extraction
Year: 2026
Type: conference-paper
Abstract: Event extraction requires performing two interdependent subtasks: event detection and event argument extraction.While prior work has explored pipelined and joint training approaches, the question of how best to coordinate training across these subtasks in generative LLM-based systems remains open.We present a systematic study comparing three training paradigms: disjoint, fully shared and hybrid weight allocation, instantiated as eight concrete strategies and evaluated on ACE2005 and RichERE across multiple instruction-tuned LLMs.Our findings show that training strategy has a consistent and meaningful effect on extraction accuracy, and that a clear best-performing strategy emerges across models and benchmarks.We believe that these findings could extend beyond event extraction to other information extraction tasks that decompose into interdependent subtasks.
Authors: Rishi Ravikumar, Riza Batista-Navarro
Institution: University of Manchester
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166802834