Extraction and Classification of Statute Facets using Few-shot Learning

Sachin Pawar, Basit Ali, Girish K. Palshikar, Ramandeep Singh, Dhirendra Singh

Abstract: In this paper, we focus on automatic extraction of statute facets from legal statutes such as Act documents. We define statute facets to be key specific aspects of a statute which can potentially be used in legal arguments. For example, Section 25F of the Industrial Disputes Act (India) contains statute facets such as workman, employer, retrenchment of workmen, continuous service for not less than one year, etc. Such statute facets are often used by lawyers as part of their argumentation and also by judges for deciding on a case. In this paper, we propose a weakly supervised technique for extracting such statute facets from legal text. We use dependency tree structure to extract candidate statute facets and use BM25 ranking function to determine statute-specificity of these candidates. We propose a set of facet types which enable us to realize the definition of statute facets in a more computational way. We use recent deep learning models in a few-shot setting to predict an appropriate facet type for each candidate. Only those candidates with high statute-specificity and for which a facet type is predicted with high confidence, are selected as acceptable statute facets. We evaluate the extracted statute facets through both direct and indirect evaluation as well as conduct a user-study to get validation and feedback from lawyers.