Další formáty:
BibTeX
LaTeX
RIS
@inproceedings{1777697,
author = {Šavelka, Jaromír and Westermann, Hannes and Benyekhlef, Karim and Alexander, Charlotte S. and Grant, Jayla C. and Amariles, Restrepo David and Hamdani, Rajaa El and Meeùs, Sébastien and Troussel, Aurore and Araszkiewicz, Michal and Ashley, Kevin D. and Ashley, Alexandra and Branting, Karl and Falduti, Mattia and Grabmair, Matthias and Harašta, Jakub and Novotná, Tereza and Tippett, Elizabeth and Johnson, Shiwanni},
address = {New York},
booktitle = {Eighteenth International Conference on Artificial Intelligence and Law: Proceedings of the Conference},
doi = {http://dx.doi.org/10.1145/3462757.3466149},
edition = {1},
keywords = {multi-lingual sentence embeddings; transfer learning; domain adaptation; adjudicatory decisions; document segmentation; annotation},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York},
isbn = {978-1-4503-8526-8},
pages = {129-138},
publisher = {ACM},
title = {Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains},
url = {https://dl.acm.org/doi/10.1145/3462757.3466149},
year = {2021}
}
TY - JOUR
ID - 1777697
AU - Šavelka, Jaromír - Westermann, Hannes - Benyekhlef, Karim - Alexander, Charlotte S. - Grant, Jayla C. - Amariles, Restrepo David - Hamdani, Rajaa El - Meeùs, Sébastien - Troussel, Aurore - Araszkiewicz, Michal - Ashley, Kevin D. - Ashley, Alexandra - Branting, Karl - Falduti, Mattia - Grabmair, Matthias - Harašta, Jakub - Novotná, Tereza - Tippett, Elizabeth - Johnson, Shiwanni
PY - 2021
TI - Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
PB - ACM
CY - New York
SN - 9781450385268
KW - multi-lingual sentence embeddings
KW - transfer learning
KW - domain adaptation
KW - adjudicatory decisions
KW - document segmentation
KW - annotation
UR - https://dl.acm.org/doi/10.1145/3462757.3466149
N2 - In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contexts). Mechanisms for utilizing linguistic resources outside of their original context have significant potential benefits in AI & Law because differences between legal systems, languages, or traditions often block wider adoption of research outcomes. We analyze the use of LanguageAgnostic Sentence Representations in sequence labeling models using Gated Recurrent Units (GRUs) that are transferable across languages. To investigate transfer between different contexts we developed an annotation scheme for functional segmentation of adjudicatory decisions. We found that models generalize beyond the contexts on which they were trained (e.g., a model trained on administrative decisions from the US can be applied to criminal law decisions from Italy). Further, we found that training the models on multiple contexts increases robustness and improves overall performance when evaluating on previously unseen contexts. Finally, we found that pooling the training data from all the contexts enhances the models’ in-context performance.
ER -
ŠAVELKA, Jaromír, Hannes WESTERMANN, Karim BENYEKHLEF, Charlotte S. ALEXANDER, Jayla C. GRANT, Restrepo David AMARILES, Rajaa El HAMDANI, Sébastien MEEÙS, Aurore TROUSSEL, Michal ARASZKIEWICZ, Kevin D. ASHLEY, Alexandra ASHLEY, Karl BRANTING, Mattia FALDUTI, Matthias GRABMAIR, Jakub HARAŠTA, Tereza NOVOTNÁ, Elizabeth TIPPETT a Shiwanni JOHNSON. Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains. Online. In \textit{Eighteenth International Conference on Artificial Intelligence and Law: Proceedings of the Conference}. 1. vyd. New York: ACM, 2021, s.~129-138. ISBN~978-1-4503-8526-8. Dostupné z: https://dx.doi.org/10.1145/3462757.3466149.
|
