[2204.00806] HLDC: Hindi Legal Documents Corpus
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Computer Science > Computation and Language

arXiv:2204.00806 (cs)
[Submitted on 2 Apr 2022 (v1), last revised 24 May 2024 (this version, v2)]

Title:HLDC: Hindi Legal Documents Corpus

Authors:Arnav Kapoor, Mudit Dhawan, Anmol Goel, T.H. Arjun, Akshala Bhatnagar, Vibhu Agrawal, Amul Agrawal, Arnab Bhattacharya, Ponnurangam Kumaraguru, Ashutosh Modi
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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. We release the corpus and model implementation code with this paper: this https URL
Comments: 16 Pages, Accepted at ACL 2022 Findings
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2204.00806 [cs.CL]
  (or arXiv:2204.00806v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2204.00806

Submission history

From: Ashutosh Modi [view email]
[v1] Sat, 2 Apr 2022 08:22:52 UTC (4,902 KB)
[v2] Fri, 24 May 2024 11:07:12 UTC (4,903 KB)
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