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

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


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 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%.
Anthology ID:
2021.nllp-1.5
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–62
Language:
URL:
https://aclanthology.org/2021.nllp-1.5
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.nllp-1.5.pdf
Data
Universal Dependencies