Artificial Intelligence and Law最新文献

LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals LegalAnalytics：在巴西最高法院上诉中衔接视觉解释和工作量简化

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-07-11 DOI: 10.1007/s10506-025-09446-w

Lucas Resck, Felipe Moreno-Vera, Tobias Veiga, Gerardo Paucar, Ezequiel Fajreldines, Guilherme Klafke, Luis G. Nonato, Jorge Poco

{"title":"LegalAnalytics: bridging visual explanations and workload streamline in Brazilian Supreme Court appeals","authors":"Lucas Resck, Felipe Moreno-Vera, Tobias Veiga, Gerardo Paucar, Ezequiel Fajreldines, Guilherme Klafke, Luis G. Nonato, Jorge Poco","doi":"10.1007/s10506-025-09446-w","DOIUrl":"10.1007/s10506-025-09446-w","url":null,"abstract":"<div><p>The Brazilian Supreme Court serves as the highest judicial authority in Brazil and is responsible for adjudicating constitutional matters presented as extraordinary appeals. These appeals undergo a rigorous screening process guided by established legal principles known as Topics of General Repercussion. Seeking to streamline this procedure, we developed LegalAnalytics to explore the research question: <i>Can machine learning and explainable AI techniques enhance the classification of appeals in legal workflows?</i> LegalAnalytics harnesses advanced natural language processing algorithms and classification models to categorize each appeal according to the most pertinent topics accurately. In addition, it incorporates LIME (Local Interpretable Model-agnostic Explanations) to highlight the key sections of an appeal and compare them with relevant precedents. This approach ensures a transparent justification for every classification. The system is thoughtfully designed with a user-friendly interface tailored for public servants, judges, and lawyers. Extensive testing with dozens of legal experts confirmed the effectiveness of LegalAnalytics, with consistently positive feedback underscoring its significant practical impact.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"685 - 743"},"PeriodicalIF":5.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-025-09446-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

R2GQA: retriever-reader-generator question answering system to support students understanding legal regulations in higher education R2GQA：检索-读者-生成问答系统，支持学生理解高等教育中的法律法规

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-05-22 DOI: 10.1007/s10506-025-09457-7

Tinh Pham Phuc Do, Ngoc Dinh Duy Cao, Khanh Quoc Tran, Kiet Van Nguyen

{"title":"R2GQA: retriever-reader-generator question answering system to support students understanding legal regulations in higher education","authors":"Tinh Pham Phuc Do, Ngoc Dinh Duy Cao, Khanh Quoc Tran, Kiet Van Nguyen","doi":"10.1007/s10506-025-09457-7","DOIUrl":"10.1007/s10506-025-09457-7","url":null,"abstract":"<div><p>In this article, we propose the R2GQA system, a retriever-reader-generator question answering system, consisting of three main components: Document Retriever, Machine Reader, and Answer Generator. The Document Retriever module employs advanced information retrieval techniques to extract the context of articles from a dataset of legal regulation documents. The Machine Reader module utilizes state-of-the-art natural language understanding algorithms to comprehend the retrieved documents and extract answers. Finally, the Generator module synthesizes the extracted answers into concise and informative responses to questions of students regarding legal regulations. Furthermore, we built the ViRHE4QA dataset in the domain of university training regulations, comprising 9,758 question-answer pairs with a rigorous construction process. This is the first Vietnamese dataset in the higher regulations domain with various types of answers, both extractive and abstractive. In addition, the R2GQA system is the first system to offer abstractive answers in Vietnamese. This paper discusses the design and implementation of each module within the R2GQA system on the ViRHE4QA dataset, highlighting their functionalities and interactions. Furthermore, we present experimental results demonstrating the effectiveness and utility of the proposed system in supporting the comprehension of students of legal regulations in higher education settings. In general, the R2GQA system and the ViRHE4QA dataset promise to contribute significantly to related research and help students navigate complex legal documents and regulations, empowering them to make informed decisions and adhere to institutional policies effectively. Our dataset is available for research purposes.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"923 - 968"},"PeriodicalIF":5.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated consistency analysis for legal contracts 法律合同的自动一致性分析

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-05-13 DOI: 10.1007/s10506-025-09456-8

Alan Khoja, Martin Kölbl, Stefan Leue, Rüdiger Wilhelmi

{"title":"Automated consistency analysis for legal contracts","authors":"Alan Khoja, Martin Kölbl, Stefan Leue, Rüdiger Wilhelmi","doi":"10.1007/s10506-025-09456-8","DOIUrl":"10.1007/s10506-025-09456-8","url":null,"abstract":"<div><p>Business contracts, particularly sale and purchase agreements, often contain a large number of clauses and are correspondingly long and complex. In practice, it is therefore a great challenge to keep track of their legal context and to identify and avoid inconsistencies in such contracts. Against this background, we describe a method and tool called <i>ContractCheck</i> which allows for the consistency analysis of legal contracts, in particular share purchase agreements (SPAs). In order to identify the concepts that are relevant for an analysis we define an ontology for SPAs. The analysis is, then, based on an encoding of the preconditions for the execution of the clauses of an SPA, as well as on a set of proposed consistency constraints formalized using decidable fragments of first-order logic (FOL). Based on the ontology for SPAs, textual SPAs are first encoded in a structured natural language format that we refer to as “blocks”. <i>ContractCheck</i> interprets these blocks and constraints and translates them into assertions formulated in FOL. It then invokes a Satisfiability Modulo Theory (SMT) solver in order to check the executability of a considered contract, either by providing a satisfying model, or by proving the existence of conflicting clauses that prevent the contract from being executed. We illustrate the application of <i>ContractCheck</i> to concrete SPAs, including one example of an SPA of realistic size and complexity, and conclude by suggesting directions for future research.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"881 - 922"},"PeriodicalIF":5.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-025-09456-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extractive summarization of Indian legal judgements using unsupervised role labelling 使用无监督角色标签的印度法律判决摘录摘要

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-05-06 DOI: 10.1007/s10506-025-09453-x

Shruti Shreyasi, Ayan Bandyopadhyay, Partha Pratim Chakrabarti

{"title":"Extractive summarization of Indian legal judgements using unsupervised role labelling","authors":"Shruti Shreyasi, Ayan Bandyopadhyay, Partha Pratim Chakrabarti","doi":"10.1007/s10506-025-09453-x","DOIUrl":"10.1007/s10506-025-09453-x","url":null,"abstract":"<div><p>Extractive summaries of legal judgments are invaluable due to their ability to preserve the original text, facilitating direct reference when required. Existing research on extractive summarization of Indian legal judgments predominantly employs supervised methodologies, which rely on extensive and often difficult-to-obtaining expert annotations. State-of-the-art methods that use role labels or rhetorical role labels are largely limited to supervised learning paradigms (Bhattacharya et al. in Identification of rhetorical roles of sentences in indian legal judgments, 2019). In this study, we propose an unsupervised approach, Unsupervised Role-Labelled Knapsack Summarizer (<b>URL KnapSum</b>), which eliminates the need for expert annotations while effectively scaling to larger datasets. Our methodology begins by clustering sentences to group them into thematic categories. An optimized selection of sentences is performed using a knapsack algorithm using the similarity scores and length constraints, ensuring cluster-level diversity and global coherence. We evaluate the URL KnapSum using ROUGE (Lin in ROUGE: a package for automatic evaluation of summaries. In: Text summarization branches out, Barcelona, Spain. Association for Computational Linguistics, pp 74–81, 2004) scores for supervised evaluation and Kendall’s tau (Puka in Kendall’s Tau. Springer, Berlin, Heidelberg, pp 713–715, 2011) and Spearman’s correlation (Sedgwick in BMJ: Br Med J 349:g7327, 2014) for unsupervised evaluation. Additionally, we introduce a novel, reference-free evaluation metric, the <b>Top-K Analysis Metric</b>, which benchmarks the algorithm by measuring consistency between document and summary similarities. The results demonstrate the superiority of URL KnapSum over existing supervised methods, highlighting its effectiveness and scalability.\u0000</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"815 - 845"},"PeriodicalIF":5.3,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring evidential phenomena for complex reasoning patterns about a mass of evidence 测量大量证据的复杂推理模式的证据现象

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-05-03 DOI: 10.1007/s10506-025-09455-9

Patrick Juchli, Franco Taroni, Colin Aitken

{"title":"Measuring evidential phenomena for complex reasoning patterns about a mass of evidence","authors":"Patrick Juchli, Franco Taroni, Colin Aitken","doi":"10.1007/s10506-025-09455-9","DOIUrl":"10.1007/s10506-025-09455-9","url":null,"abstract":"<div><p>Studies on the evidential foundations of probabilistic reasoning are extended using the notion of weight of evidence to measure evidential phenomena in the presence of a mass of evidence giving rise to complex reasoning patterns. The main results provided in this paper are methods to measure inferential interactions and dissonances among items of evidence. All measures are defined to ensure a versatile applicability in inferential tasks involving the combination of evidence and a mass of evidence. These measures enable a detailed examination of recurrent phenomena in evidence-based reasoning, such as convergence, contradiction, redundancy, and synergy. Most of these phenomena have—as far as the authors are aware—either not been formally described or any formal description proposed has been of limited use for evidence-based reasoning tasks. The present research addresses this deficit in the current understanding and treatment of these evidential phenomena. It is shown by way of examples that incorrect consideration of evidential phenomena can lead to substantial misrepresentations of the value of evidence.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"847 - 880"},"PeriodicalIF":5.3,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-025-09455-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Summarizing judicial documents: a hybrid extractive- abstractive model with legal domain knowledge 司法文书摘要：具有法律领域知识的抽取-抽象混合模型

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2025-04-22 DOI: 10.1007/s10506-025-09435-z

Yan Gao, Jie Wu, Zhengtao Liu, Juan Li

{"title":"Summarizing judicial documents: a hybrid extractive- abstractive model with legal domain knowledge","authors":"Yan Gao, Jie Wu, Zhengtao Liu, Juan Li","doi":"10.1007/s10506-025-09435-z","DOIUrl":"10.1007/s10506-025-09435-z","url":null,"abstract":"<div><p>The automatic summarization of judgment documents is a challenging task due to their length and the dispersed nature of the important information they contain. The prevailing approach to tackling the summarization of lengthy documents involves the integration of both extractive and abstractive summarization models. However, current extractive models face challenges in capturing all essential details due to the scattered distribution of pertinent information within judgment documents. Additionally, the existing abstractive models still grapple with the problem of \"hallucinations\" which leads to generating inaccurate information. In our work, we proposed a novel hybrid legal summarization method that incorporates legal domain knowledge into both the extractive model and abstractive model. The method consists of two parts: (1) The rhetorical role of sentences is identified by the sentence-level sequence labeling method, and the rhetorical information is integrated into the extractive model based on WoBERT through the conditional normalization to ensure that the identification of key sentences is both precise and complete. (2) The pre-trained model RoFormer is combined with Seq2Seq to construct a long text summarization model, and the prior knowledge in the external resources and the document itself is introduced into the decoding process to improve the faithfulness and coherence of the composed summary. In addition, the contrastive learning strategy is employed during the training process to enhance the robustness of the abstractive model. Experimental results on the CAIL2020 dataset show that the proposed model is superior to the baseline methods. Furthermore, our method outperforms GPT and other LLMs in processing judgment documents.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 2","pages":"493 - 521"},"PeriodicalIF":3.1,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating legal question generation using large language models 使用大型语言模型研究法律问题生成

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-04-15 DOI: 10.1007/s10506-025-09452-y

Aniket Deroy, Kripabandhu Ghosh, Saptarshi Ghosh

{"title":"Investigating legal question generation using large language models","authors":"Aniket Deroy, Kripabandhu Ghosh, Saptarshi Ghosh","doi":"10.1007/s10506-025-09452-y","DOIUrl":"10.1007/s10506-025-09452-y","url":null,"abstract":"<div><p>We propose the task of <i>legal question generation</i> (QG) as an application in Legal NLP. Specifically, the task is to generate a question, given a context and an optional keyword. We create the first dataset for the QG task in the legal domain, called <i>LegalQ</i>, consisting of 2023 <context, question> pairs spanning the legal systems of multiple countries, and multiple languages. We then use this dataset to benchmark several Large Language Models (LLMs) including Turbo-GPT-3.5, GPT-4, Llama2-70b, Llama2-13b, and Aalap-Mistral-7b (a legal domain-specific LLM). We also fine-tune several open-source LLMs such as T5, BART, Pegasus, and Flan-T5, which helps to improve results over zero-shot prompting of LLMs. We also use the idea of in-context learning (via few-shot examples) to generate questions of varying types and difficulty levels. Furthermore, we introduce a novel domain-specific prompting strategy based on chain-of-thoughts prompting for question generation. Further, we perform Bloom Taxonomy analysis of the questions generated by the LLMs, thereby showing that ‘understanding’ and ‘remembering’ are the two most dominant types of questions generated by the LLMs. Human evaluation of the generated questions shows promise in terms of generating grammatically correct, relevant, appropriate, complex, and novel questions. Finally, we analyze the incomplete or unanswerable generated questions to find possible reasons for these issues.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"775 - 813"},"PeriodicalIF":5.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating interdisciplinary approaches in legal education: a study on enhancing problem-solving, innovation, and comprehensive legal understanding 在法律教育中整合跨学科方法：加强问题解决、创新和全面法律理解的研究

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-04-07 DOI: 10.1007/s10506-025-09445-x

Xiaojun Dai

{"title":"Integrating interdisciplinary approaches in legal education: a study on enhancing problem-solving, innovation, and comprehensive legal understanding","authors":"Xiaojun Dai","doi":"10.1007/s10506-025-09445-x","DOIUrl":"10.1007/s10506-025-09445-x","url":null,"abstract":"<div><p>An interdisciplinary approach to legal education is a process of educating law that encompasses courses from other disciplines. An interdisciplinary course is essential for law students to improve complete outlook on legal issues. The interdisciplinary approach creates effective legal findings. The study objective is to explore the role of interdisciplinary approaches to revolutionizing legal education for the 21st century, focused on the benefits of legal education to enhance students’ problem-solving skills and innovative thinking. Both quantitative and qualitative research methods were used in this study. A total number of 800 structured questionnaires were distributed to individuals. The data collection lasted nearly 27 days. 785 valid responses were received from the participants. The data were analyzed using SPSS software. The valid responses are used to recognize the role of interdisciplinary approaches in legal education. The research found that interdisciplinary approaches in legal education develop students’ problem-solving skills and innovative thinking. The interdisciplinary approach helps to improve students’ ability to solve complex problems comprehensively. Legal education increases graduates’ ability to adapt to the changing demands of the 21st-century legal profession. This study enhances law students’ problem-solving skills through interdisciplinary approaches in legal education. The study highlights innovative thinking, understanding of complex legal issues, graduates’ adaptability, and greater competence. This study’s novelty lies in scrutinizing the advantages of interdisciplinary approaches to revolutionizing legal education for the 21st century.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"657 - 683"},"PeriodicalIF":5.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Indian legal judgment classification with embeddings, feature selection, and ensemble strategies 用嵌入、特征选择和集成策略增强印度法律判决分类

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2025-03-24 DOI: 10.1007/s10506-025-09438-w

Priyanka Prabhakar, Peeta Basa Pati

{"title":"Enhancing Indian legal judgment classification with embeddings, feature selection, and ensemble strategies","authors":"Priyanka Prabhakar, Peeta Basa Pati","doi":"10.1007/s10506-025-09438-w","DOIUrl":"10.1007/s10506-025-09438-w","url":null,"abstract":"<div><p>Legal document analysis presents significant challenges due to its complexity and domain-specific nature. This study introduces an innovative approach for classifying Indian court judgments into legal domains using diverse machine learning and deep learning techniques. The method incorporates feature engineering and deep learning algorithms for extracting meaningful features, supported by a wide range of classifiers, including voting classifiers, gradient boosting, and random forest. Embeddings are generated using models such as InLegalBERT, InCaseLawBERT, CustomInLawBERT, Mamba, T5, RoBERTa, CodeT5, SBERT, DistilBERT, XLM, XLM Large, LegalBERT, GPT2, ALBERT, Electra, DeBERTa, TFDeBERTa, FlanT5, FlanT-Large, BART, BigBird Pegasus, LongFormer, and LUKE. To address class imbalance, the SMOTE technique is employed, and dimensionality is reduced using PCA and forward feature selection. The T5+SMOTE+feature selection+voting classifier configuration achieves a notable accuracy of 98%, highlighting the effectiveness of the proposed approach. These advancements have significant implications for applications such as document retrieval, legal discovery, and case law analysis, enhancing the accuracy and efficiency of legal document classification.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 2","pages":"523 - 564"},"PeriodicalIF":3.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147968148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MARRO: multi-headed attention for rhetorical role labeling in legal documents 法律文件中修辞角色标注的多重注意

IF 5.3 2区社会学

Artificial Intelligence and Law Pub Date : 2025-03-18 DOI: 10.1007/s10506-025-09449-7

Purbid Bambroo, Subinay Adhikary, Paheli Bhattacharya, Abhijnan Chakraborty, Saptarshi Ghosh, Kripabandhu Ghosh

{"title":"MARRO: multi-headed attention for rhetorical role labeling in legal documents","authors":"Purbid Bambroo, Subinay Adhikary, Paheli Bhattacharya, Abhijnan Chakraborty, Saptarshi Ghosh, Kripabandhu Ghosh","doi":"10.1007/s10506-025-09449-7","DOIUrl":"10.1007/s10506-025-09449-7","url":null,"abstract":"<div><p>Identification of rhetorical roles like facts, arguments, and final judgments is central to understanding a legal case document and can lend power to other downstream tasks like legal case summarization and judgment prediction. However, there are several challenges to this task. Legal documents are often unstructured and contain a specialized vocabulary, making it hard for conventional transformer models to understand them. Additionally, these documents run into several pages, which makes it difficult for neural models to capture the entire context at once. Lastly, there is a dearth of annotated legal documents to train deep learning models. Previous state-of-the-art approaches for this task have focused on using neural models like BiLSTM-CRF or have explored different embedding techniques to achieve decent results. While such techniques have shown that better embedding can result in improved model performance, not many models have focused on utilizing attention for learning better embeddings in sentences of a document. Additionally, it has been recently shown that advanced techniques like multi-task learning can help the models learn better representations, thereby improving performance. In this paper, we combine these two aspects by proposing a novel family of multi-task learning-based models for rhetorical role labeling, named <span>MARRO</span>, that uses transformer-inspired multi-headed attention. Using label shift as an auxiliary task, we show that models from the <span>MARRO</span> family achieve state-of-the-art results on two labeled datasets for rhetorical role labeling, from the Indian and UK Supreme Courts.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"34 3","pages":"745 - 774"},"PeriodicalIF":5.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149078881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0