ignore at S em E val-2024 Task 5: A Legal Classification Model with Summary Generation and Contrastive Learning (2024)

Abstract: This paper describes our work for SemEval-2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. After analyzing the task requirements and the training dataset, we used data augmentation, adopted the large model GPT for summary generation, and added supervised contrastive learning to the basic BERT model. Our system achieved an F1 score of 0.551, ranking 14th in the competition leaderboard. Our system achieves an F1 score improvement of 0.1241 over the official baseline model.

## introduction in task 5 of semeval-2024: the legal argument reasoning task in civil procedure @xcite , we expect to reason about legal arguments in civil actions, as shown in the foundation model we choose is legal-bert @xcite , which collects different english legal texts from multiple domains (e.g., legislation, court cases, contracts) for pre-training. compared with other models such as legal-roberta @xcite , it can handle this task data better. based on that, a great variety of strategies have been tested along with our exploration, such as summary generation, data augmentation (da), and contrastive learning. data analysis for this task revealed that the dataset size was relatively small (only 666 entries), yet each data point contains substantial information. in such a language environment, we realize using and enriching data fully is very important. we used generative summarization, contrastive learn-ing, and data augmentation to train the model, which led to our system ranking 14th in this task. ## related work legal information is mostly expressed in the form of text, such as legal cases, bills, contracts, legislation, and so on. therefore, legal text processing is an important area of nlp, including classifying legal topics @xcite , generating rulings based on what the court has already done @xcite , etc. in the past, some traditional machine learning methods like svm bag of words @xcite performed worse than neural models on legal tasks. the use of generic pre-trained models becomes the new paradigm, such as legall longformer @xcite and italian-legal-bert @xcite . data augmentation is a mature method for expanding a dataset when there is little training data, and in this case, we are not using external data but rather taking full advantage of the various fields of the provided data. task 5 is a small sample task, and we adopt contrastive learning to distinguish them from different samples by grouping similar samples together, hoping to learn from the intrinsic structure of the data. we use triples as a loss function @xcite , and according to the characteristics of our task, we use a supervised contrast learning @xcite algorithm, where the triples are anchor points, positive samples, and negative samples. ## system overview our baseline system simply feeds legal-bert with two pieces of text, classifies its output [cls] tokens, and scores their similarity with the humanannotated data by cross-entropy loss training. all the optimized strategies discussed below are based on this framework, and the overall framework of our final system is shown in figure 1 . after train-key value introduction my students always get confused about the relationship between removal to federal court and personal jurisdiction. suppose that a defendant is sued in arizona and believes that she is not subject to personal jurisdiction there .[...]fed. r. civ. p. 4(k)(1)(a). i've stumped a multitude of students on this point. consider the following two cases to clarify the point. ing with all positive policies, we ensemble the best model on each fold for the final prediction. ## conclusion by deploying various optimization methods, including data augmentation, summary generation, and supervised contrastive learning, we build a conceivably powerful system to reason about the task of legal argumentation in civil litigation. and ranked 14th in the evaluation stage competition with a 0.551 f1 score in the officially organized competition. in future work, one is that law is a serious domain, and we plan to guide the model by prior knowledge. we also plan to incorporate domainspecific knowledge into the exercises and analyses of the law school textbooks under study. second, we consider whether we can better model long texts by using tools external to the model to assist in processing long texts and optimizing the model.