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

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    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%.
    Original languageEnglish
    Title of host publicationProceedings of the Natural Legal Language Processing Workshop 2021
    EditorsNikolaos Aletras, Ion Androutsopoulos, Leslie Barrett, Catalina Goanta, Daniel Preotiuc-Pietro
    PublisherAssociation for Computational Linguistics (ACL)
    Pages46-62
    Number of pages17
    Publication statusPublished - 2021
    EventNatural Legal Language Processing Workshop 2021 -
    Duration: 10-Nov-2021 → …
    https://nllpw.org/workshop/

    Conference

    ConferenceNatural Legal Language Processing Workshop 2021
    Period10/11/2021 → …
    Internet address

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