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2021 EMNLP EMNLP 2021

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

Abstract

AbstractThe 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 com- pare 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 im- plemented across these countries. We evalu- ated multiple multi-label classifiers on a manu- ally annotated corpus at sentence level. The XLM-RoBERTa model achieves highest per- formance on this multilingual multi-label clas- sification task, with a macro-average F1 score of 59.8%.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — exceptional measure
🐣 Hot Topic Early Bird — legal document
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors

Georgios Tziafas , Eugenie de Saint-Phalle , Wietse de Vries , Clara Egger , Tommaso Caselli

Topics

Natural Language Processing > Applications > Text Classification Natural Language Processing > Resources & Methods > Large Language Models Data Science & Analytics > Applications > Disease Surveillance Machine Learning > Learning Types > Multi-Label Classification Artificial Intelligence > Core AI > Natural Language Processing Deep Learning > Learning Types > Multi-Label Classification

Keywords

multilingual nlp multi-label classification multilingual transformer legal document legal text classification covid-19 analysis legal document classification exceptional measure
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