Indian Legal NLP Benchmarks : A Survey

Availability of challenging benchmarks is the key to advancement of AI in a specific field.Since Legal Text is significantly different than normal English text, there is a need to create separate Natural Language Processing benchmarks for Indian Legal Text which are challenging and focus on tasks specific to Legal Systems. This will spur innovation in applications of Natural language Processing for Indian Legal Text and will benefit AI community and Legal fraternity. We review the existing work in this area and propose ideas to create new benchmarks for Indian Legal Natural Language Processing

Named Entity Recognition in Indian court judgments

Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.Comment: to be published in NLLP 2022 Workshop at EMNL

Transition From Unilamellar to Bilameller Vesicles Induced by an Amphiphilic Biopolymer

We report some unusual structural transitions upon the addition of an amphiphilic biopolymer to unilamellar surfactant vesicles. The polymer is a hydrophobically modified chitosan and it embeds its hydrophobes in vesicle bilayers. We study vesicle-polymer mixtures using small-angle neutron scattering (SANS) and cryotransmission electron microscopy (cryo-TEM). When low amounts of the polymer are added to unilamellar vesicles of ca. 120 nm diameter, the vesicle size decreases by about 50%. Upon further addition of polymer, lamellar peaks are observed in the SANS spectra at high scattering vectors. We show that these spectra correspond to a co-existence of unilamellar and bilamellar vesicles. The transition to bilamellar vesicles as well as the changes in unilamellar vesicle size are further confirmed by cryo-TEM. A mechanism for the polymer-induced transitions in vesicle morphology is proposed

Corpus for Automatic Structuring of Legal Documents

In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.Comment: Accepted at LREC 2022, 10 Pages (8 page main paper + 2 page references

Full-length VP2 gene analysis of canine parvovirus reveals emergence of newer variants in India

The canine parvovirus (CPV) infection is a highly contagious and serious enteric disease of dogs with high fatality rate. The present study was taken up to characterize the full-length viral polypeptide 2 (VP2) gene of CPV of Indian origin along with the commercially available vaccines. The faecal samples from parvovirus suspected dogs were collected from various states of India for screening by PCR assay and 66.29% of samples were found positive. Six CPV-2a, three CPV-2b, and one CPV-2c types were identified by sequence analysis. Several unique and existing mutations have been noticed in CPV types analyzed indicating emergence of newer variants of CPV in India. The phylogenetic analysis revealed that all the field CPV types were grouped in different subclades within two main clades, but away from the commercial vaccine strains. CPV-2b and CPV-2c types with unique mutations were found to be establishing in India apart from the prevailing CPV-2a type. Mutations and the positive selection of the mutants were found to be the major mechanism of emergence and evolution of parvovirus. Therefore, the incorporation of local strain in the vaccine formulation may be considered for effective control of CPV infections in India

IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given the linguistic diversity, high-quality and accessible Machine Translation (MT) systems are essential in a country like India. Prior to this work, there was (i) no parallel training data spanning all the 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India. In this work, we aim to address this gap by focusing on the missing pieces required for enabling wide, easy, and open access to good machine translation systems for all 22 scheduled Indian languages. We identify four key areas of improvement: curating and creating larger training datasets, creating diverse and high-quality benchmarks, training multilingual models, and releasing models with open access. Our first contribution is the release of the Bharat Parallel Corpus Collection (BPCC), the largest publicly available parallel corpora for Indic languages. BPCC contains a total of 230M bitext pairs, of which a total of 126M were newly added, including 644K manually translated sentence pairs created as part of this work. Our second contribution is the release of the first n-way parallel benchmark covering all 22 Indian languages, featuring diverse domains, Indian-origin content, and source-original test sets. Next, we present IndicTrans2, the first model to support all 22 languages, surpassing existing models on multiple existing and new benchmarks created as a part of this work. Lastly, to promote accessibility and collaboration, we release our models and associated data with permissive licenses at https://github.com/ai4bharat/IndicTrans2

SemEval 2023 Task 6: LegalEval -- Understanding Legal Texts

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.Comment: 13 Pages (9 Pages + References), Accepted at SemEval 202

Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly-available parallel corpora, and additionally mine 37.4 million sentence pairs from the web, resulting in a 4x increase. We mine the parallel sentences from the web by combining many corpora, tools, and methods: (a) web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar, which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at https://indicnlp.ai4bharat.org/samanantar/ and we hope they will help advance research in NMT and multilingual NLP for Indic languages.Comment: Accepted to the Transactions of the Association for Computational Linguistics (TACL