Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector

Rajdeep Sarkar, Atul Kr. Ojha, Jay Megaro, John Mariano, Vall Herard, John P. McCrae


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.
Anthology ID:
2021.nllp-1.10
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
102–106
Language:
URL:
https://aclanthology.org/2021.nllp-1.10
DOI:
10.18653/v1/2021.nllp-1.10
Bibkey:
Cite (ACL):
Rajdeep Sarkar, Atul Kr. Ojha, Jay Megaro, John Mariano, Vall Herard, and John P. McCrae. 2021. Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 102–106, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector (Sarkar et al., NLLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2021.nllp-1.10.pdf