Natural Language Processing in the legal domain been benefited hugely by the
emergence of Transformer-based Pre-trained Language Models (PLMs) pre-trained
on legal text. There exist PLMs trained over European and US legal text, most
notably LegalBERT. However, with the rapidly increasing volume of NLP
applications on Indian legal documents, and the distinguishing characteristics
of Indian legal text, it has become necessary to pre-train LMs over Indian
legal text as well. In this work, we introduce transformer-based PLMs
pre-trained over a large corpus of Indian legal documents. We also apply these
PLMs over several benchmark legal NLP tasks over both Indian legal text, as
well as over legal text belonging to other domains (countries). The NLP tasks
with which we experiment include Legal Statute Identification from facts,
Semantic segmentation of court judgements, and Court Judgement Prediction. Our
experiments demonstrate the utility of the India-specific PLMs developed in
this work.

本文介绍了在印度法律文本上进行预训练的基于 Transformer 的 PLMs，并在多个基准法律 NLP 任务上运用了这些 PLMs，结果表明这些印度特定的 PLMs 非常有用。