MEE: A Novel Multilingual Event Extraction Dataset

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9603 - 9613 December 7-11, 2022 ©2022 Association for Computational Linguistics MEE: A Novel Multilingual Event Extraction Dataset Amir Pouran Ben Veyseh1, Javid Ebrahimi1, Franck Dernoncourt2, and Thien Huu Nguyen1 1Department of Computer Science, University of Oregon, Eugene, OR, USA 2Adobe Research, Seattle, WA, USA {apouranb, thien}@cs.uoregon.edu; jebivid@gmail.com franck.dernoncourt@adobe.com Abstract 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. 1 Introduction Event Extraction (EE) is one of the major tasks of Information Extraction (IE) for text. In a complete EE pipeline, three major goals should be pursued: (1) Entity Mention Detection (EMD): to recognize mentions of real world entities; (2) Event Detection (ED): to identify event mentions/triggers and their types. An event trigger is a word or phrase that most clearly refers to the occurrence of an event; and (3) Event Argument Extraction (EAE): to find participants/arguments of an event mentioned in text. A participant is an entity mention that has an specific role in a given event mention. For in- stance, in the sentence “The soldiers were hit by the forces.”, there are two entity mentions “sol- diers” and “forces” of types PERSON and ORGA- NIZATION and an event trigger “hit” of type AT- TACK. Also, the two event mentions “soldiers” and “forces” play the argument roles of Victim and At- tacker (respectively) in the ATTACK event. An EE system could be employed in other downstream applications such as Question Answering, Knowl- edge Base Population and Text Summarization to assist extracting information about events in text. Multiple methods have been proposed for Event Extraction. Early work has employed feature-based models (Ahn, 2006; Ji and Grishman, 2008; Liao and Grishman, 2010; Hong et al., 2011; Li et al., 2013; Yang and Mitchell, 2016) while later meth- ods have explored deep learning to present state-of- the-art performance for Event Extraction (Nguyen and Grishman, 2015; Chen et al., 2015; Nguyen et al., 2016; Sha et al., 2018; Wang et al., 2019; Lai et al., 2020; Veyseh et al., 2020; Lin et al., 2020; Nguyen et al., 2021a; Liu et al., 2022). How- ever, despite all advancements on event extraction in recent years, a major limitation of current EE research is to overly focus …

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