# EventFull: Complete and Consistent Event Relation Annotation

arXiv: https://arxiv.org/abs/2412.12733 · HTML: https://mlsanity.com/paper/2412.12733/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [Neural Language Modeling for Contextualized Temporal Graph Generation](https://mlsanity.com/paper/2010.10077/) (2020)
- [CoRefi: A Crowd Sourcing Suite for Coreference Annotation](https://mlsanity.com/paper/2010.02588/) (2020)
- [Context-aware Event Forecasting via Graph Disentanglement](https://mlsanity.com/paper/2308.06480/) (2023)
- [TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles](https://mlsanity.com/paper/2310.17802/) (2023)
- [Identifying Conspiracy Theories News based on Event Relation Graph](https://mlsanity.com/paper/2310.18545/) (2023)
- [Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation](https://mlsanity.com/paper/2404.01532/) (2024)
- [ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction](https://mlsanity.com/paper/2309.12892/) (2023)
- [NarrativeTime: Dense Temporal Annotation on a Timeline](https://mlsanity.com/paper/1908.11443/) (2019)
- [Unveiling Divergent Inductive Biases of LLMs on Temporal Data](https://mlsanity.com/paper/2404.01453/) (2024)

## Built on by (most cited first, 2 of 2)

- [Beyond Pairwise: Global Zero-shot Temporal Graph Generation](https://mlsanity.com/paper/2502.11114/) (2025)
- [Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives](https://mlsanity.com/paper/2610.17402/) (2026)

## Abstract (arXiv)

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 textitEventFull, 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.