Identifying/retrieving relevant statutes and prior cases/precedents for a given legal situation are common tasks exercised by law practitioners.Researchers to date have addressed the two tasks independently, thus developing completely different datasets and models for each task; however, both retrieval tasks are inherently related, e.g., similar cases tend to cite similar statutes (due to similar factual situation).In this paper, we address this gap.We propose IL-PCSR (Indian Legal corpus for Prior Case and Statute Retrieval), which is a unique corpus that provides a common testbed for developing models for both the tasks (Statute Retrieval and Precedent Retrieval) that can exploit the dependence between the two.We experiment extensively with several baseline models on the tasks, including lexical models, semantic models and ensemble based on GNNs.Further, to exploit the dependence between the two tasks, we develop an LLM-based re-ranking approach that gives the best performance.

Abhinav Joshi, Shounak Paul, Akshat Sharma, Pawan Goyal, Saptarshi Ghosh, Ashutosh Modi. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Legal Statute Identification (LSI) involves identifying the relevant statutes (articles of law) given the facts (evidence) of a legal case. There are several key challenges in LSI, such as (i)~usage of label (statute) semantics which can be complicated and confusing; (ii)~the input text (i.e., the facts) are very long and noisy; (iii)~the label distribution usually follows a long tail, making predictions for the rare labels challenging. Although multiple methods have been proposed to address these challenges, there has not been any comprehensive study to establish the effects of these factors on different models/approaches. In this work, we reproduce several LSI models on two popular LSI datasets and study the effect of the above-mentioned challenges. We conduct thorough experiments with transformer-based encoders such as BERT and Longformer. We further try out different combinations of these encoders with approaches devised specifically for LSI, which essentially use different mechanisms to model the statute texts to enhance fact representations. Our experiments yield several interesting insights into how the above-mentioned challenges are addressed by different models, the interplay of different encoding and statute text handling measures, and how the nature of the LSI datasets affects the model performances. Finally, we also analyze the explanability capabilities of different approaches using human-annotated rationales.

NLP in the legal domain has seen increasing success with the emergence of Transformer-based Pre-trained Language Models (PLMs) pre-trained on legal text. PLMs trained over European and US legal text are available publicly; however, legal text from other domains (countries), such as India, have a lot of distinguishing characteristics. With the rapidly increasing volume of Legal NLP applications in various countries, it has become necessary to pre-train such LMs over legal text of other countries as well. In this work, we attempt to investigate pre-training in the Indian legal domain. We re-train (continue pre-training) two popular legal PLMs, LegalBERT and CaseLawBERT, on Indian legal data, as well as train a model from scratch with a vocabulary based on Indian legal text. We apply these PLMs over three benchmark legal NLP tasks - Legal Statute Identification from facts, Semantic Segmentation of Court Judgment Documents, and Court Appeal Judgment Prediction - over both Indian and non-Indian (EU, UK) datasets. We observe that our approach not only enhances performance on the new domain (Indian texts) but also over the original domain (European and UK texts). We also conduct explainability experiments for a qualitative comparison of all these different PLMs.

The task of Legal Statute Identification (LSI) aims to identify the legal statutes that are relevant to a given description of facts or evidence of a legal case. Existing methods only utilize the textual content of facts and legal articles to guide such a task. However, the citation network among case documents and legal statutes is a rich source of additional information, which is not considered by existing models. In this work, we take the first step towards utilising both the text and the legal citation network for the LSI task. We curate a large novel dataset for this task, including facts of cases from several major Indian Courts of Law, and statutes from the Indian Penal Code (IPC). Modeling the statutes and training documents as a heterogeneous graph, our proposed model LeSICiN can learn rich textual and graphical features, and can also tune itself to correlate these features. Thereafter, the model can be used to inductively predict links between test documents (new nodes whose graphical features are not available to the model) and statutes (existing nodes). Extensive experiments on the dataset show that our model comfortably outperforms several state-of-the-art baselines, by exploiting the graphical structure along with textual features.

Automatic Charge Identification (ACI) is the task of identifying the relevant legal charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process.Prior works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations.We observe that only a small fraction of sentences in the facts actually indicates the charges.We show that by using a very small subset (< 3%) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset.Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework.The proposed model comprehensively outperforms a large number of recent baseline models for ACI.The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.

Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on. The task is challenging since legal case documents are usually not well-structured, and these rhetorical roles may be subjective (as evident from variation of opinions between legal experts). In this paper, we address this task for judgments from the Supreme Court of India. We label sentences in 50 documents using multiple human annotators, and perform an extensive analysis of the human-assigned labels. We also attempt automatic identification of the rhetorical roles of sentences. While prior approaches towards this task used Conditional Random Fields over manually handcrafted features, we explore the use of deep neural models which do not require hand-crafting of features. Experiments show that neural models perform much better in this task than baseline methods which use handcrafted features.