FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing

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33 Citations (Scopus)

Original language	English
Journal	Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)
Volume	1
Pages (from-to)	4389-4406
Number of pages	18
DOIs	https://doi.org/10.18653/v1/2022.acl-long.301
Publication status	Published - 2022

Access to Document

@inproceedings{bf9e4af208c14e18b00622880a1880f2,

title = "FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing",

author = "Ilias Chalkidis and Tommaso Pasini and Sheng Zhang and Letizia Tomada and Schwemer, {Sebastian Felix} and Anders S{\o}gaard",

year = "2022",

doi = "10.18653/v1/2022.acl-long.301",

language = "English",

volume = "1",

pages = "4389--4406",

journal = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)",

}

TY - GEN

T1 - FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing

AU - Chalkidis, Ilias

AU - Pasini, Tommaso

AU - Zhang, Sheng

AU - Tomada, Letizia

AU - Schwemer, Sebastian Felix

AU - Søgaard, Anders

PY - 2022

Y1 - 2022

U2 - 10.18653/v1/2022.acl-long.301

DO - 10.18653/v1/2022.acl-long.301

M3 - Conference article

VL - 1

SP - 4389

EP - 4406

JO - Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)

JF - Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)

ER -