Publication: LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain
LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain
Date
Date
Date
Citations
Niklaus, J., Matoshi, V., Rani, P., Galassi, A., Stürmer, M., & Chalkidis, I. (2023). LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain. Findings of the Association for Computational Linguistics, 3016–3054. https://doi.org/10.18653/v1/2023.findings-emnlp.200
Abstract
Abstract
Abstract
Lately, propelled by phenomenal advances around the transformer architecture, the legal NLP field has enjoyed spectacular growth. To measure progress, well-curated and challenging benchmarks are crucial. Previous efforts have produced numerous benchmarks for general NLP models, typically based on news or Wikipedia. However, these may not fit specific domains such as law, with its unique lexicons and intricate sentence structures. Even though there is a rising need to build NLP systems for languages other than English, many benchmarks
Metrics
Additional indexing
Creators (Authors)
- Niklaus, Joel
- Matoshi, Veton
- Galassi, Andrea
- Stürmer, Matthias
- Chalkidis, Ilias
Event Title
Event Title
Event Title
Event Location
Event Location
Event Location
Event Country
Event Country
Event Country
Event Start Date
Event Start Date
Event Start Date
Event End Date
Event End Date
Event End Date
Page range/Item number
Page range/Item number
Page range/Item number
Page end
Page end
Page end
Item Type
Item Type
Item Type
In collections
Dewey Decimal Classifikation
Dewey Decimal Classifikation
Dewey Decimal Classifikation
Language
Language
Language
Date available
Date available
Date available
Series Name
Series Name
Series Name
OA Status
OA Status
OA Status
Free Access at
Free Access at
Free Access at
Publisher DOI
Metrics
Citations
Niklaus, J., Matoshi, V., Rani, P., Galassi, A., Stürmer, M., & Chalkidis, I. (2023). LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain. Findings of the Association for Computational Linguistics, 3016–3054. https://doi.org/10.18653/v1/2023.findings-emnlp.200
