HLDC: Hindi Legal Documents Corpus

Arnav Kapoor, Mudit Dhawan, Anmol Goel, Arjun T H, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi


Abstract
Many populous countries including India are burdened with a considerable backlog of legal cases. Development of automated systems that could process legal documents and augment legal practitioners can mitigate this. However, there is a dearth of high-quality corpora that is needed to develop such data-driven systems. The problem gets even more pronounced in the case of low resource languages such as Hindi. In this resource paper, we introduce the Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents in Hindi. Documents are cleaned and structured to enable the development of downstream applications. Further, as a use-case for the corpus, we introduce the task of bail prediction. We experiment with a battery of models and propose a Multi-Task Learning (MTL) based model for the same. MTL models use summarization as an auxiliary task along with bail prediction as the main task. Experiments with different models are indicative of the need for further research in this area.
Anthology ID:
2022.findings-acl.278
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3521–3536
Language:
URL:
https://aclanthology.org/2022.findings-acl.278
DOI:
10.18653/v1/2022.findings-acl.278
Bibkey:
Cite (ACL):
Arnav Kapoor, Mudit Dhawan, Anmol Goel, Arjun T H, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, and Ashutosh Modi. 2022. HLDC: Hindi Legal Documents Corpus. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3521–3536, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
HLDC: Hindi Legal Documents Corpus (Kapoor et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.278.pdf
Software:
 2022.findings-acl.278.software.zip
Code
 exploration-lab/hldc