Standard
LexGLUE : A Benchmark Dataset for Legal Language Understanding in English. / Chalkidis, Ilias; Jana, Abhik; Hartung, Dirk; Bommarito, Michael; Androutsopoulos, Ion; Katz, Daniel Martin; Aletras, Nikolaos.
ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). red. / Smaranda Muresan; Preslav Nakov; Aline Villavicencio. Association for Computational Linguistics, 2022. s. 4310-4330 (Proceedings of the Annual Meeting of the Association for Computational Linguistics, Bind 1).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Chalkidis, I, Jana, A, Hartung, D, Bommarito, M, Androutsopoulos, I, Katz, DM & Aletras, N 2022, LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. i S Muresan, P Nakov & A Villavicencio (red), ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics, Proceedings of the Annual Meeting of the Association for Computational Linguistics, bind 1, s. 4310-4330, 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022, Dublin, Irland, 22/05/2022.
APA
Chalkidis, I., Jana, A., Hartung, D., Bommarito, M., Androutsopoulos, I., Katz, D. M., & Aletras, N. (2022). LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. I S. Muresan, P. Nakov, & A. Villavicencio (red.), ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (s. 4310-4330). Association for Computational Linguistics. Proceedings of the Annual Meeting of the Association for Computational Linguistics Bind 1
Vancouver
Chalkidis I, Jana A, Hartung D, Bommarito M, Androutsopoulos I, Katz DM o.a. LexGLUE: A Benchmark Dataset for Legal Language Understanding in English. I Muresan S, Nakov P, Villavicencio A, red., ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics. 2022. s. 4310-4330. (Proceedings of the Annual Meeting of the Association for Computational Linguistics, Bind 1).
Author
Chalkidis, Ilias ; Jana, Abhik ; Hartung, Dirk ; Bommarito, Michael ; Androutsopoulos, Ion ; Katz, Daniel Martin ; Aletras, Nikolaos. / LexGLUE : A Benchmark Dataset for Legal Language Understanding in English. ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). red. / Smaranda Muresan ; Preslav Nakov ; Aline Villavicencio. Association for Computational Linguistics, 2022. s. 4310-4330 (Proceedings of the Annual Meeting of the Association for Computational Linguistics, Bind 1).
Bibtex
@inproceedings{70afd6f0a68e4df49c4b47ed2b6e49e3,
title = "LexGLUE: A Benchmark Dataset for Legal Language Understanding in English",
abstract = "Laws and their interpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.",
author = "Ilias Chalkidis and Abhik Jana and Dirk Hartung and Michael Bommarito and Ion Androutsopoulos and Katz, {Daniel Martin} and Nikolaos Aletras",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 ; Conference date: 22-05-2022 Through 27-05-2022",
year = "2022",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
pages = "4310--4330",
editor = "Smaranda Muresan and Preslav Nakov and Aline Villavicencio",
booktitle = "ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",
publisher = "Association for Computational Linguistics",
}
RIS
TY - GEN
T1 - LexGLUE
T2 - 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022
AU - Chalkidis, Ilias
AU - Jana, Abhik
AU - Hartung, Dirk
AU - Bommarito, Michael
AU - Androutsopoulos, Ion
AU - Katz, Daniel Martin
AU - Aletras, Nikolaos
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - Laws and their interpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
AB - Laws and their interpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
UR - http://www.scopus.com/inward/record.url?scp=85137748584&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85137748584
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 4310
EP - 4330
BT - ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
A2 - Muresan, Smaranda
A2 - Nakov, Preslav
A2 - Villavicencio, Aline
PB - Association for Computational Linguistics
Y2 - 22 May 2022 through 27 May 2022
ER -
