MEE: A Novel Multilingual Event Extraction Dataset

Autor:	Veyseh, Amir Pouran Ben, Ebrahimi, Javid, Dernoncourt, Franck, Nguyen, Thien Huu
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at EMNLP 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.05955 Zobrazit plný text záznamu View this record from Arxiv