Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification. / Xenouleas, Stratos; Tsoukara, Alexia; Panagiotakis, Giannis; Chalkidis, Ilias; Androutsopoulos, Ion.

Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022. Association for Computing Machinery, Inc., 2022. 19 (ACM International Conference Proceeding Series).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Xenouleas, S, Tsoukara, A, Panagiotakis, G, Chalkidis, I & Androutsopoulos, I 2022, Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification. i Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022., 19, Association for Computing Machinery, Inc., ACM International Conference Proceeding Series, 12th Hellenic Conference on Artificial Intelligence, SETN 2022, Corfu, Grækenland, 07/09/2022. https://doi.org/10.1145/3549737.3549760

APA

Xenouleas, S., Tsoukara, A., Panagiotakis, G., Chalkidis, I., & Androutsopoulos, I. (2022). Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification. I Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022 [19] Association for Computing Machinery, Inc.. ACM International Conference Proceeding Series https://doi.org/10.1145/3549737.3549760

Vancouver

Xenouleas S, Tsoukara A, Panagiotakis G, Chalkidis I, Androutsopoulos I. Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification. I Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022. Association for Computing Machinery, Inc. 2022. 19. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3549737.3549760

Author

Xenouleas, Stratos ; Tsoukara, Alexia ; Panagiotakis, Giannis ; Chalkidis, Ilias ; Androutsopoulos, Ion. / Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification. Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022. Association for Computing Machinery, Inc., 2022. (ACM International Conference Proceeding Series).

Bibtex

@inproceedings{3595e63970704446bb9f78d473009a8c,
title = "Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification",
abstract = "We consider zero-shot cross-lingual transfer in legal topic classification using the recent Multi-EURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual transfer, we develop a new version of the dataset without parallel documents. We use it to show that translation-based methods vastly outperform cross-lingual fine-tuning of multilingually pre-trained models, the best previous zero-shot transfer method for Multi-EURLEX. We also develop a bilingual teacher-student zero-shot transfer approach, which exploits additional unlabeled documents of the target language and performs better than a model fine-tuned directly on labeled target language documents. ",
keywords = "legal text classification, natural language processing, zero-shot cross-lingual transfer learning",
author = "Stratos Xenouleas and Alexia Tsoukara and Giannis Panagiotakis and Ilias Chalkidis and Ion Androutsopoulos",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 12th Hellenic Conference on Artificial Intelligence, SETN 2022 ; Conference date: 07-09-2022 Through 09-09-2022",
year = "2022",
doi = "10.1145/3549737.3549760",
language = "English",
series = "ACM International Conference Proceeding Series",
booktitle = "Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022",
publisher = "Association for Computing Machinery, Inc.",

}

RIS

TY - GEN

T1 - Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification

AU - Xenouleas, Stratos

AU - Tsoukara, Alexia

AU - Panagiotakis, Giannis

AU - Chalkidis, Ilias

AU - Androutsopoulos, Ion

N1 - Publisher Copyright: © 2022 ACM.

PY - 2022

Y1 - 2022

N2 - We consider zero-shot cross-lingual transfer in legal topic classification using the recent Multi-EURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual transfer, we develop a new version of the dataset without parallel documents. We use it to show that translation-based methods vastly outperform cross-lingual fine-tuning of multilingually pre-trained models, the best previous zero-shot transfer method for Multi-EURLEX. We also develop a bilingual teacher-student zero-shot transfer approach, which exploits additional unlabeled documents of the target language and performs better than a model fine-tuned directly on labeled target language documents.

AB - We consider zero-shot cross-lingual transfer in legal topic classification using the recent Multi-EURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual transfer, we develop a new version of the dataset without parallel documents. We use it to show that translation-based methods vastly outperform cross-lingual fine-tuning of multilingually pre-trained models, the best previous zero-shot transfer method for Multi-EURLEX. We also develop a bilingual teacher-student zero-shot transfer approach, which exploits additional unlabeled documents of the target language and performs better than a model fine-tuned directly on labeled target language documents.

KW - legal text classification

KW - natural language processing

KW - zero-shot cross-lingual transfer learning

UR - http://www.scopus.com/inward/record.url?scp=85138412890&partnerID=8YFLogxK

U2 - 10.1145/3549737.3549760

DO - 10.1145/3549737.3549760

M3 - Article in proceedings

AN - SCOPUS:85138412890

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the 12th Hellenic Conference on Artificial Intelligence, SETN 2022

PB - Association for Computing Machinery, Inc.

T2 - 12th Hellenic Conference on Artificial Intelligence, SETN 2022

Y2 - 7 September 2022 through 9 September 2022

ER -

ID: 342927381