@inproceedings{sarkar-etal-2021-shot,
title = "Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector",
author = "Sarkar, Rajdeep and
Ojha, Atul Kr. and
Megaro, Jay and
Mariano, John and
Herard, Vall and
McCrae, John P.",
editor = "Aletras, Nikolaos and
Androutsopoulos, Ion and
Barrett, Leslie and
Goanta, Catalina and
Preotiuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.10",
doi = "10.18653/v1/2021.nllp-1.10",
pages = "102--106",
abstract = "The application of predictive coding techniques to legal texts has the potential to greatly reduce the cost of legal review of documents, however, there is such a wide array of legal tasks and continuously evolving legislation that it is hard to construct sufficient training data to cover all cases. In this paper, we investigate few-shot and zero-shot approaches that require substantially less training data and introduce a triplet architecture, which for promissory statements produces performance close to that of a supervised system. This method allows predictive coding methods to be rapidly developed for new regulations and markets.",
}
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%0 Conference Proceedings
%T Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector
%A Sarkar, Rajdeep
%A Ojha, Atul Kr.
%A Megaro, Jay
%A Mariano, John
%A Herard, Vall
%A McCrae, John P.
%Y Aletras, Nikolaos
%Y Androutsopoulos, Ion
%Y Barrett, Leslie
%Y Goanta, Catalina
%Y Preotiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F sarkar-etal-2021-shot
%X The application of predictive coding techniques to legal texts has the potential to greatly reduce the cost of legal review of documents, however, there is such a wide array of legal tasks and continuously evolving legislation that it is hard to construct sufficient training data to cover all cases. In this paper, we investigate few-shot and zero-shot approaches that require substantially less training data and introduce a triplet architecture, which for promissory statements produces performance close to that of a supervised system. This method allows predictive coding methods to be rapidly developed for new regulations and markets.
%R 10.18653/v1/2021.nllp-1.10
%U https://aclanthology.org/2021.nllp-1.10
%U https://doi.org/10.18653/v1/2021.nllp-1.10
%P 102-106
Markdown (Informal)
[Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector](https://aclanthology.org/2021.nllp-1.10) (Sarkar et al., NLLP 2021)
ACL