EventFull: Complete and Consistent Event Relation Annotation
This addresses the problem of systematic and complete event relation annotation for NLP researchers and practitioners, though it is incremental as it builds on existing annotation needs.
The authors tackled the challenge of costly and incomplete annotation of event relations in NLP by introducing EventFull, a tool that supports consistent and efficient annotation of temporal, causal, and coreference relations, accelerating the process and achieving high inter-annotator agreement.
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.