GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering

Pre-trained language models have shown remarkable performance in recent years, setting a new paradigm for natural language processing (NLP) research.The legal domain has received some attention from the NLP community, in part due to its textual nature.Question answering (QA) systems represent some of the tasks in this domain.This work explores the legal multiple-choice QA (MCQA) for Romanian.The contribution of this work is multifold.We introduce JuRO, the first openly available Romanian legal MCQA dataset, comprising 10,836 questions from three examinations.Along with this dataset, we introduce CROL, an organized corpus of laws comprising a total of 93 distinct documents with their modifications over 763 time spans, which we used for information retrieval techniques in this work.Additionally, we construct Law-RoG, the first graph of legal knowledge for the Romanian language, derived from the aforementioned corpus.Lastly, we propose a novel approach for MCQA, namely Graph Retrieval Augmented by Facts (GRAF), which achieves competitive results with generally accepted state-of-the-art methods and even exceeds them in most settings.