Legal Judgment Prediction for Canadian Appeal Cases

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University of Ottawa
Law is one of the knowledge domains that are most reliant on textual material. In this age of legal big data, and with the increased availability of legal text online, many researchers started working on the development of legal intelligent systems and applications. These intelligent systems can provide great services and solve many problems in the legal domain. Over the last few years, researchers have focused on predicting judicial case outcomes using Natural Language Processing (NLP) and Machine Learning (ML) methods over case documents. Thus, Legal Judgment Prediction (LJP) is the task of automatically predicting the outcome of a court case given the case description. To the best of our knowledge, no prior research with this intention has been conducted in English for appeal courts in Canada, as of 2023. The NLP application to legal judgments, that our proposed methodology focuses on, is to predict the outcomes of cases by looking only at the text of cases. Because appeal court decisions are often binary, as in ’Allow’ or ’Dismiss’, the task is defined as a binary classification problem. This is the general approach in the literature as well. However, many of the previous LJP approaches utilized traditional classifiers or standard general language models (LMs). In our thesis, we constructed a Canadian Appeal-Law dataset (CanAL-DS) that contains a collection of decisions from different higher courts in Canada. In addition, we further pre-trained the LegalBERT model on our collected corpus that combines around 50,000 documents of Canadian case law and legislation which resulted in (CanAL) LegalBERT, a Canadian Appeal-Law BERT-based legal model. Moreover, we proposed a novel Ensemble-Hierarchical CanAL (EH-CanAL) architecture that simulates the actual voting setting in appellate courts showing great promise in LJP performance within Canadian case law. We improved the architecture with a multi-task component (MEH-CanAL) to help the model identify what legal paragraphs require the most attention and facilitate its explainability. Results from our study demonstrate the potential for the proposed approaches to reshape traditional judicial decision-making and the efficacy of domain-specific language models. Through this study, we hope to establish the basis for future research on the appellate law system of Canada and offer a baseline for future work.
Legal, Appeals, NLP, Transformer, Multitasking, Explainability