A Multilingual Approach to Identify and Classify Exceptional Measures Against COVID-19

Abstract

The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact. This work presents the initial results of an on-going project, EXCEPTIUS, aiming to automatically identify, classify and compare exceptional measures against COVID-19 across 32 countries in Europe. To this goal, we created a corpus of legal documents with sentence-level annotations of eight different classes of exceptional measures that are implemented across these countries. We evaluated multiple multi-label classifiers on a manually annotated corpus at sentence level. The XLM-RoBERTa model achieves highest performance on this multilingual multi-label classification task, with a macro-average F1 score of 59.8%.