MEE: A Novel Multilingual Event Extraction Dataset

TL;DR

This paper introduces MEE, a comprehensive multilingual event extraction dataset covering over 50,000 event mentions across 8 languages, aiming to advance non-English event extraction research.

Contribution

The paper presents a new high-quality multilingual EE dataset with extensive annotations, addressing the lack of resources for non-English languages in event extraction.

Findings

Identifies challenges in multilingual EE

Provides baseline results for the dataset

Highlights opportunities for future research

Abstract

Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE.