ARC - NLP at CASE 2022 Task 1: Ensemble Learning for Multilingual Protest Event Detection (2022)

Abstract: Automated socio-political protest event detection is a challenging task when multiple languages are considered. In CASE 2022 Task 1, we propose ensemble learning methods for multilingual protest event detection in four subtasks with different granularity levels from document-level to entity-level. We develop an ensemble of fine-tuned Transformer-based language models, along with a post-processing step to regularize the predictions of our ensembles. Our approach places the first place in 6 out of 16 leaderboards organized in seven languages including English, Mandarin, and Turkish.

## introduction socio-political protest events are organized to protest against various decision and policy makers. an example is the social movement of arab springs and internet hacktivism. the detection of socio-political protest events in news articles is a challenging task when news are reported in multiple languages. the shared task of multilingual protest news detection @xcite , organized in the workshop of challenges and applications of automated extraction of socio-political events from text (case) that is held at the 2022 conference on empirical methods in natural language processing (emnlp), targets automated detection of protest events considering language generalization of the event information collection systems. the shared task includes four subtasks: subtask 1, protest document classification: the subtask aims to detect if news articles contain past or ongoing protest events. there are three source languages; english, spanish, and portuguese. in addition, there are seven target languages including english, turkish, and mandarin. the granularity of classification is document-level. the prediction output is binary (protest exists or not). subtask 2, protest sentence classification: the subtask aims to detect if the news sentences contain protest events. there are three source and target languages; english, spanish, and portuguese. the granularity of classification is sentence-level. the prediction output is binary. ## discussion and conclusion in this study, we summarize our solutions for multilingual protest event detection under four subtasks that have different granularities from document to word span-level. our overall approach is based on ensemble learning and post-processing, which places the first place in 6 out of 16 leaderboards organized in seven languages including english, mandarin, and turkish. based on the experiments and leaderboard results, we have the following observations.