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
- Editors:
- Nikolaos Aletras, Ion Androutsopoulos, Leslie Barrett, Catalina Goanta, Daniel Preotiuc-Pietro
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46–62
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2021.nllp-1.5/
- DOI:
- 10.18653/v1/2021.nllp-1.5
- Cite (ACL):
- Georgios Tziafas, Eugenie de Saint-Phalle, Wietse de Vries, Clara Egger, and Tommaso Caselli. 2021. A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 46–62, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19 (Tziafas et al., NLLP 2021)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2021.nllp-1.5.pdf
- Data
- Universal Dependencies