Semi-automatic Triage of Requests for Free Legal Assistance

Meladel Mistica, Jey Han Lau, Brayden Merrifield, Kate Fazio, Timothy Baldwin


Abstract
Free legal assistance is critically under-resourced, and many of those who seek legal help have their needs unmet. A major bottleneck in the provision of free legal assistance to those most in need is the determination of the precise nature of the legal problem. This paper describes a collaboration with a major provider of free legal assistance, and the deployment of natural language processing models to assign area-of-law categories to real-world requests for legal assistance. In particular, we focus on an investigation of models to generate efficiencies in the triage process, but also the risks associated with naive use of model predictions, including fairness across different user demographics.
Anthology ID:
2021.nllp-1.23
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
217–227
Language:
URL:
https://aclanthology.org/2021.nllp-1.23
DOI:
10.18653/v1/2021.nllp-1.23
Bibkey:
Cite (ACL):
Meladel Mistica, Jey Han Lau, Brayden Merrifield, Kate Fazio, and Timothy Baldwin. 2021. Semi-automatic Triage of Requests for Free Legal Assistance. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 217–227, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Semi-automatic Triage of Requests for Free Legal Assistance (Mistica et al., NLLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nllp-1.23.pdf