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        <title>Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs</title>
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        <namePart type="given">Hugo</namePart>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2024</title>
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            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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            <namePart type="given">Ilias</namePart>
            <namePart type="family">Chalkidis</namePart>
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            <namePart type="given">Leslie</namePart>
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            <namePart type="given">Daniel</namePart>
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            <namePart type="given">Gerasimos</namePart>
            <namePart type="family">Spanakis</namePart>
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    <abstract>This paper presents a novel approach to legal judgment prediction by combining BERT embeddings with a Delaunay-based Graph Neural Network (GNN). Unlike inductive methods that classify legal documents independently, our transductive approach models the entire document set as a graph, capturing both contextual and relational information. This method significantly improves classification accuracy by enabling effective label propagation across connected documents. Evaluated on the Swiss-Judgment-Prediction (SJP) dataset, our model outperforms established baselines, including larger models with cross-lingual training and data augmentation techniques, while maintaining efficiency with minimal computational overhead.</abstract>
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%0 Conference Proceedings
%T Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs
%A Attali, Hugo
%A Tomeh, Nadi
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F attali-tomeh-2024-transductive
%X This paper presents a novel approach to legal judgment prediction by combining BERT embeddings with a Delaunay-based Graph Neural Network (GNN). Unlike inductive methods that classify legal documents independently, our transductive approach models the entire document set as a graph, capturing both contextual and relational information. This method significantly improves classification accuracy by enabling effective label propagation across connected documents. Evaluated on the Swiss-Judgment-Prediction (SJP) dataset, our model outperforms established baselines, including larger models with cross-lingual training and data augmentation techniques, while maintaining efficiency with minimal computational overhead.
%U https://aclanthology.org/2024.nllp-1.15
%P 187-193

Markdown (Informal)

[Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs](https://aclanthology.org/2024.nllp-1.15) (Attali & Tomeh, NLLP 2024)

• Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs (Attali & Tomeh, NLLP 2024)

ACL

• Hugo Attali and Nadi Tomeh. 2024. Transductive Legal Judgment Prediction Combining BERT Embeddings with Delaunay-Based GNNs. In Proceedings of the Natural Legal Language Processing Workshop 2024, pages 187–193, Miami, FL, USA. Association for Computational Linguistics.