NLP -Titan at S em E val-2023 Task 6: Identification of Rhetorical Roles Using Sequential Sentence Classification (2023)

Abstract: The analysis of legal cases poses a considerable challenge for researchers, practitioners, and academicians due to the lengthy and intricate nature of these documents. Developing countries such as India are experiencing a significant increase in the number of pending legal cases, which are often unstructured and difficult to process using conventional methods. To address this issue, the authors have implemented a sequential sentence classification process, which categorizes legal documents into 13 segments, known as Rhetorical Roles. This approach enables the extraction of valuable insights from the various classes of the structured document. The performance of this approach was evaluated using the F1 score, which measures the model’s precision and recall. The authors’ approach achieved an F1 score of 0.83, which surpasses the baseline score of 0.79 established by the task organizers. The authors have combined sequential sentence classification and the SetFit method in a hierarchical manner by combining similar classes to achieve this score.

## introduction legal cases are often complex, lengthy and intricate, posing significant challenges for researchers, practitioners, and academicians alike. these challenges become even more pronounced in developing countries like india, where the number of legal cases is growing exponentially. the national judicial data grid of india @xcite shows more than 43 million pending legal cases. these result in legal documents that are unstructured and highly complex, making them difficult to process efficiently. this requires modern solutions like new-age technology and ai advancements to help process this vast backlog of pending legal cases efficiently. legal documents differ considerably from the text on which pre-trained natural language pro-cessing (nlp) models are trained. legal documents are typically large, unstructured, and include legal jargon, as well as numerous mistakes. additionally, different categories of legal cases are fundamentally different from one another. to overcome this challenge, nlp models trained on accurate well-annotated corpus are necessary. however, creating such a corpus itself is challenging, resulting in slow growth in the legal nlp domain. to efficiently process long legal documents, this paper proposes a method for rhetorical role classification using nlp techniques to segment legal texts from legal documents into semantically coherent classes. the proposed method is a text segmenting task which involves classifying parts or sentences of a document. the task uses a corpus of annotated legal documents, which have the spans of the document annotated with the appropriate class or rhetorical role. sample rhetorical roles have been depicted in figure 1 . a method to merge similar classes at the base level was implemented to aide the actual task. the sequence sentence classification was carried out using sequential sentence classifier and setfit method is used to classify the merged classes. in the segmentation of these rhetorical roles from legal documents. this method for rhetorical role classification using nlp techniques provides a solution to process long legal documents efficiently. however, creating an accurate and well-annotated corpus of data is a significant challenge in the legal nlp domain. the proposed method can help to overcome this challenge by segmenting legal texts from legal documents into semantically coherent classes, enabling the efficient processing of legal documents. ## background research with the development in the field of deep learning, legal text processing has been an active topic of research. some of the examples of work in legal domain are summarization of legal documents @xcite summarization for legal judgment and segmenting rhetorical roles @xcite , headnote generation @xcite the classification of scientific articles based on their rhetorical role is an active research area in natural language processing. this approach involves identifying the function or purpose of individual sentences or paragraphs within a scientific article and then using this information to categorize the article into a specific rhetorical role. teufel and moens @xcite made a significant contribution to scientific discourse by introducing the concept of rhetorical role for scientific articles. in their research paper, they presented a hierarchical model that identified different levels of granularity, such as sections, paragraphs, and sentences within scientific articles. this model was used to classify scientific articles into one of six rhetorical zones representing distinct communicative functions. in the field of scientific discourse analysis, another method for classifying scientific articles based on rhetorical roles involves the use of argumentation schemes. palau and moens @xcite proposed an approach that employed argumentation schemes to determine the function of individual sentences within scientific articles. their research paper demonstrated the effectiveness of this approach in identifying the rhetorical roles of scientific articles. by utilizing argumentation schemes, palau and moens' method provides a structured and systematic way of analyzing the persuasive elements of scientific discourse. in @xcite , authors cat-egorized rhetorical roles into seven distinct categories and compared the effectiveness of neural network-based approaches with traditional methods. these results revealed that the neural networkbased approach outperformed the traditional methods in identifying the rhetorical roles. the rhetorical role task can also be seen as sequence sentence classification, i.e. classifying a document with various classes. previously, hierarchical sequence encoders such as lstms @xcite were utilized for encoding individual sentences and contextualizing these encodings. afterwards, a crf was applied on top of the contextualized encodings as seen in @xcite which used medical data from @xcite . ## system overview the proposed method in this paper explores the usage of modern natural language processing techniques and models, namely, hierarchical sequential labeling network (hsln) @xcite and bert @xcite base embeddings to accomplish the task of sequential sentence classification into rhetorical roles from a legal document. the following text describes the abovementioned techniques. ## results and conclusion prior to attaining optimal performance, diverse transformer-based models were employed. the model underwent training over a span of 70 epochs, utilizing an adam optimizer with a learning rate of 3e-4. to preclude overfitting and excessive gpu usage, the authors implemented a scheduler mechanism whereby the learning rate automatically diminishes if the f1 score does not exhibit improvement for five consecutive epochs. with further research and development, the proposed method can potentially improve the efficiency of legal systems in developing countries like india, where the backlog of pending legal cases is ever-increasing. the method for segmentation of rhetorical roles can also further help in data extraction tasks, summarization tasks, judgment prediction and other such tasks as legal entity recognition.