Explainable Natural Language Inference in the Legal Domain via Text Generation
Jungmin Choi, Ukyo Honda, Taro Watanabe, Kentaro Inui · Transactions of the Japanese Society for Artificial Intelligence · 2023
Natural language inference (NLI) in the legal domain is the task of predicting entailment between the premise, i.e. law, and the hypothesis, which is a statement regarding a legal issue. Current state-of-the-art approaches to NLI with pre-trained language models do not perform well in the legal domain, presumably due to a discrepancy in the level of abstraction between the premise and hypothesis and the convoluted nature of legal language. Some of the difficulties specific to the legal domain are that 1) the premise and hypothesis tend to be extensive in length; 2) the premise comprises multiple rules, and only one of the rules is related to the hypothesis. Thus only small fractions of the statements are relevant for determining entailment, while the rest is noise, and; 3) the premise is often abstract and written in legal terms, whereas the hypothesis is a concrete case and tends to be written with more ordinary vocabulary. These problems are accentuated by the scarcity of such data in the legal domain due to the high cost.