TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction

TL;DR

This paper introduces TextEE, a standardized benchmark for event extraction that addresses evaluation issues, enabling fair comparison of methods and highlighting current challenges in the field.

Contribution

It presents the first comprehensive, standardized benchmark with data preprocessing, splits, and evaluation of recent methods and large language models for event extraction.

Findings

Existing evaluation methods have inconsistencies and biases.

Recent models struggle to achieve high performance on TextEE.

The benchmark reveals significant challenges in current event extraction approaches.

Abstract

Event extraction has gained considerable interest due to its wide-ranging applications. However, recent studies draw attention to evaluation issues, suggesting that reported scores may not accurately reflect the true performance. In this work, we identify and address evaluation challenges, including inconsistency due to varying data assumptions or preprocessing steps, the insufficiency of current evaluation frameworks that may introduce dataset or data split bias, and the low reproducibility of some previous approaches. To address these challenges, we present TextEE, a standardized, fair, and reproducible benchmark for event extraction. TextEE comprises standardized data preprocessing scripts and splits for 16 datasets spanning eight diverse domains and includes 14 recent methodologies, conducting a comprehensive benchmark reevaluation. We also evaluate five varied large language models on our TextEE benchmark and demonstrate how they struggle to achieve satisfactory performance. Inspired by our reevaluation results and findings, we discuss the role of event extraction in the current NLP era, as well as future challenges and insights derived from TextEE. We believe TextEE, the first standardized comprehensive benchmarking tool, will significantly facilitate future event extraction research.