CSECU - DSG at S em E val-2023 Task 6: Segmenting Legal Documents into Rhetorical Roles via Fine-tuned Transformer Architecture (2023)

Abstract: Automated processing of legal documents is essential to manage the enormous volume of legal corpus and to make it easily accessible to a broad spectrum of people. But due to the amorphous and variable nature of legal documents, it is very challenging to directly proceed with complicated processes such as summarization, analysis, and query. Segmenting the documents as per the rhetorical roles can aid and accelerate such procedures. This paper describes our participation in SemEval-2023 task 6: Sub-task A: Rhetorical Roles Prediction. We utilize a finetuned Legal-BERT to address this task. We also conduct an error analysis to illustrate the shortcomings of our deployed approach.

## introduction analyzing and extracting critical information from legal documents poses a great challenge for researchers. legal documents can vary substantially in content, format, and structure depending on the jurisdiction, domain, and nature of documents. the corpus of legal documents is expanding rapidly, resulting in the need for automated systems for the effective retrieval of consequential information. moreover, to ensure that these legal documents are utilized efficiently and justly, they must be accessible to a wide range of people. the task benefiting from the automation of legal documents are case law analysis, summarization, semantic search, and so on. identifying the rhetorical role of each segment in the legal documents can aid in these tasks. rhetorical role identification refers to partitioning the document into semantically meaningful segments with each segment having specific roles and purposes. these rhetorical roles can be used to extract significant information for further processing and analysis. but due to legal documents being unstructured, varying from domain to domain, and the first three authors have equal contributions. in this paper, we present our deployed framework to address the challenge of predicting rhetor- ## proposed framework in this section, we elucidate our presented framework for the task of rhetorical role predictions. we exploit fine-tuned legal-bert @xcite for classifying legal sentences into 13 rhetorical roles. given a legal judgment document, we segment it into separate sentences to cast this challenge as a multi-class sequence classification task. the overview of our proposed framework is depicted in figure 2 . ## discussion in this section, we further inspect the performance of our proposed system on the development set by presenting some of the predictions in further, we investigate the performance of our system in figure 3 by presenting a statistic of correctly predicted labels against the ground truth of the development set. we can observe that in most cases, our system correctly predicted nearly the same number of rhetorical roles as the gold labels of the development set. but in some cases, such as arg_petitioner, rlc, arg_respondent, sta, pre_not_relied, and ratio, our system lacks a considerable number of differences. the difference can occur since the dataset is quite imbalanced and very few training samples are provided for some of the rhetorical roles. ## conclusion in this paper, we have described our proposed framework legal-bert. in the future, we intend to extend the corpus to include other legal domains with different structure. we also aspire to address the other two subtasks of semeval-2023 task 6.