MEE: A Novel Multilingual Event Extraction Dataset

EMNLP 2022

Publication date: December 11, 2022

Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, Thien Huu Nguyen

Event Extraction (EE) is one of the fundamen-tal 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 re-sources have been developed for Event Extrac-tion. However, one limitation of current re-search 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 hin-drance. 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 differ-ent languages. MEE comprehensively anno-tates 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. To foster future research in this direction, our dataset will be publicly available.