Abstract

In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021. The shared task has 4 subtasks which cover event detection at different granularity levels (from document level to token level) and across multiple languages (English, Hindi, Portuguese and Spanish). To handle data from multiple languages, we use a multilingual transformer-based language model (XLM-R) as the input text encoder. We apply a variety of techniques and build several transformer-based models that perform consistently well across all the subtasks and languages. Our systems achieve an average F_1 score of 81.2. Out of thirteen subtask-language tracks, our submissions rank 1st in nine and 2nd in four tracks.

Highlights

  • Event detection aims to detect and extract useful information about certain types of events from text

  • In this paper we describe our models and systems developed for “Multilingual Protest News Detection - Shared Task 1” (Hurriyetoglu et al, 2021a)

  • We presented the models and systems we developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021

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Summary

Introduction

Event detection aims to detect and extract useful information about certain types of events from text. It is an important information extraction task that discovers and gathers knowledge about past and ongoing events hidden in huge amounts of textual data. The CASE 2021 workshop (Hurriyetoglu et al, 2021b) focuses on socio-political and crisis event detection. In this paper we describe our models and systems developed for “Multilingual Protest News Detection - Shared Task 1” (Hurriyetoglu et al, 2021a). Shared task 1 in turn has 4 subtasks:

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