DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection

Hansi Hettiarachchi, Mariam Adedoyin-Olowe, Jagdev Bhogal, M. Gaber
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引用次数: 9

Abstract

Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements. In this paper, we propose an approach which can handle both document and cross-sentence level event detection in a multilingual setting using pretrained transformer models. Our approach became the winning solution in document level predictions and secured the 3rd place in cross-sentence level predictions for the English language. We could also achieve competitive results for other languages to prove the effectiveness and universality of our approach.
DAAI在CASE 2021任务1:基于转换器的多语言社会政治和危机事件检测
由于社会政治和危机事件的多样性和细微差别以及对准确性的高要求,自动社会政治和危机事件检测一直是自然语言处理以及社会政治科学界面临的挑战。在本文中,我们提出了一种方法,该方法可以使用预训练的转换器模型在多语言设置中处理文档和跨句子级别的事件检测。我们的方法成为文档级预测的获胜解决方案,并在英语的跨句子级预测中获得第三名。我们还可以在其他语言中取得竞争结果,以证明我们方法的有效性和普遍性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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