HLDC: Hindi Legal Documents Corpus
This addresses the problem of legal case backlogs in countries like India by providing a resource for developing automated systems, but it is incremental as it focuses on data creation and a specific application.
The authors tackled the lack of high-quality legal document corpora for low-resource languages like Hindi by introducing HLDC, a corpus of over 900K cleaned and structured Hindi legal documents, and demonstrated its use in bail prediction with Multi-Task Learning models, though results indicate further research is needed.
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: https://github.com/Exploration-Lab/HLDC