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 \textit{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.

翻译：事件关系检测是一项基础性的自然语言处理任务，被广泛应用于下游应用中，其建模需要标注有多种类型事件关系的数据集。然而，由于需要考虑的事件对数量呈二次方增长，对这些关系进行系统且完整的标注成本高昂且具有挑战性。因此，当前许多事件关系数据集缺乏系统性和完整性。为此，我们提出了 \textit{EventFull}，这是首个通过统一协同流程支持对时序、因果及共指关系进行一致、完整且高效标注的工具。一项试点研究表明，EventFull 在提高标注者间一致性的同时，能够加速并简化标注流程。