Code for the paper Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection accepted at EACL 2024 Findings.

While the notion of triggers should ideally be universal across domains, domain transfer for trigger detection (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).

@inproceedings{dukic-etal-2024-leveraging,
    title = "Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection",
    author = "Duki{\'c}, David  and
      Gashteovski, Kiril  and
      Glava{\v{s}}, Goran  and
      Snajder, Jan",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-eacl.80",
    pages = "1197--1213"
}