@inproceedings{eirew-etal-2025-eventfull,
    title = "{E}vent{F}ull: Complete and Consistent Event Relation Annotation",
    author = "Eirew, Alon  and
      Nachshoni, Eviatar  and
      Slobodkin, Aviv  and
      Dagan, Ido",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-demo.40/",
    doi = "10.18653/v1/2025.naacl-demo.40",
    pages = "494--508",
    ISBN = "979-8-89176-191-9",
    abstract = "Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types. However, systematic and complete annotation of these relations is costly and challenging, due to the quadratic number of event pairs that need to be considered. Consequently, many current event relation datasets lack systematicity and completeness.In response, we introduce EventFull, the first tool that supports consistent, complete and efficient annotation of temporal, causal and coreference relations via a unified and synergetic process.A pilot study demonstrates that EventFull accelerates and simplifies the annotation process while yielding high inter-annotator agreement."
}