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The research field of Legal Natural Language Processing (NLP) has been veryactive recently, with Legal Judgment Prediction (LJP) becoming one of the mostextensively studied tasks. To date, most publicly released LJP datasetsoriginate from countries with civil law. In this work, we release, for thefirst time, a challenging LJP dataset focused on class action cases in the US.It is the first dataset in the common law system that focuses on the harder andmore realistic task involving the complaints as input instead of the often usedfacts summary written by the court. Additionally, we study the difficulty ofthe task by collecting expert human predictions, showing that even humanexperts can only reach 53% accuracy on this dataset. Our Longformer modelclearly outperforms the human baseline (63%), despite only considering thefirst 2,048 tokens. Furthermore, we perform a detailed error analysis and findthat the Longformer model is significantly better calibrated than the humanexperts. Finally, we publicly release the dataset and the code used for theexperiments.

ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US