LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Creators
- 1. University of Copenhagen
- 2. Universität Hamburg
- 3. Bucerius Law School
- 4. CodeX, Stanford Law School
- 5. Athens University of Economics and Business
- 6. Illinois Tech – Chicago Kent College of Law
- 7. University of Sheffield
Description
This benchmark dataset is published with the article:
Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Martin Katz, and Nikolaos Aletras. 2021. LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. ArXiv.
Short Description
Inspired by the recent widespread use of the GLUE multi-task benchmark NLP dataset (Wang et al., 2018), the subsequent more difficult SuperGLUE (Wang et al., 2019), other previous multi-task NLP benchmarks (Conneau and Kiela,2018; McCann et al., 2018), and similar initiatives in other domains (Peng et al., 2019), we introduce LexGLUE, a benchmark dataset to evaluate the performance of NLP methods in legal tasks. LexGLUE is based on seven existing legal NLP datasets:
- ECtHR Task A (Chalkidis et al., 2019)
- ECtHR Task B (Chalkidis et al., 2021a)
- SCOTUS (Spaeth et al., 2020)
- EUR-LEX (Chalkidis et al., 2021b)
- LEDGAR (Tuggener et al. (2020)
- UNFAIR-ToS (Lippi et al., 2019)
- CaseHOLD (Zheng et al., 2021)
Files
Files
(310.2 MB)
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