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dc.contributor.advisorShevade, Shirish
dc.contributor.authorSasanka Rani, Vutla
dc.date.accessioned2024-04-03T11:07:59Z
dc.date.available2024-04-03T11:07:59Z
dc.date.submitted2024
dc.identifier.urihttps://etd.iisc.ac.in/handle/2005/6467
dc.description.abstractJudicial systems worldwide are overburdened due to the limited number of legal professionals. The digitization of legal processes has resulted in abundant legal data, paving the way for the development of legal automation systems that can assist the public and legal professionals. Legal Appeal Automation, a key problem in the legal domain, aims to automate the filing of legal appeals by using machine learning techniques to predict allegedly violated articles and provide supporting explanations based on the facts and provisions presented in the articles. Machine understanding of legal documents is challenging as they are typically lengthy, and effectively analyzing them is difficult. Further, providing explanations to justify the model predictions is complex yet crucial to building user confidence and trust in the model. Although solution approaches for predicting allegedly violated articles in legal cases have been proposed in the literature, to the best of our knowledge, no solution provides explanations justifying predictions. This absence of explanation generation is mainly due to the lack of datasets. To this end, we curate a new legal appeal automation dataset containing 9.8k instances of case- violated article pairs with explanations for each violated article. Using this dataset, we propose a novel neural architecture, Hierarchical Class Attention for Legal Appeal Automation, that efficiently handles long legal documents, predicts the allegedly violated articles and generates explanations justifying the predictions. We also introduce a baseline model for the new dataset and demonstrate that the proposed model outperforms the baseline. Using different multi- label classification datasets in the legal domain, we show that the proposed approach achieves state-of-the-art performance.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseries;ET00476
dc.rightsI grant Indian Institute of Science the right to archive and to make available my thesis or dissertation in whole or in part in all forms of media, now hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertationen_US
dc.subjectlegal automation systemsen_US
dc.subjectLegal Appeal Automationen_US
dc.subjectJudicial systemsen_US
dc.subject.classificationResearch Subject Categories::TECHNOLOGY::Information technology::Computer scienceen_US
dc.titleAn Explainable Hierarchical Class Attention Model for Legal Appeal Automationen_US
dc.typeThesisen_US
dc.degree.nameMTech (Res)en_US
dc.degree.levelMastersen_US
dc.degree.grantorIndian Institute of Scienceen_US
dc.degree.disciplineEngineeringen_US


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