Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection

• Main Authors: Dukić, David, Gashteovski, Kiril, Glavaš, Goran, Šnajder, Jan
• Format: Journal Article
• Language: English
• Published: 23-05-2023
• Subjects: Computer Science - Computation and Language; Computer Science - Learning
• DOI: 10.48550/arxiv.2305.14163

Event detection is a crucial information extraction task in many domains,
such as Wikipedia or news. The task typically relies on trigger detection (TD)
-- identifying token spans in the text that evoke specific events. While the
notion of triggers should ideally be universal across domains, domain transfer
for TD from high- to low-resource domains results in significant performance
drops. We address the problem of negative transfer in TD by coupling triggers
between domains using subject-object relations obtained from a rule-based open
information extraction (OIE) system. We demonstrate that OIE relations injected
through multi-task training can act as mediators between triggers in different
domains, enhancing zero- and few-shot TD domain transfer and reducing
performance drops, in particular when transferring from a high-resource source
domain (Wikipedia) to a low(er)-resource target domain (news). Additionally, we
combine this improved transfer with masked language modeling on the target
domain, observing further TD transfer gains. Finally, we demonstrate that the
gains are robust to the choice of the OIE system.