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        <title>Automatic Linking of Judgements to UK Supreme Court Hearings</title>
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    <abstract>One the most important archived legal material in the UK is the Supreme Court published judgements and video recordings of court sittings for the decided cases. The impact of Supreme Court published material extends far beyond the parties involved in any given case as it provides landmark rulings on arguable points of law of the greatest public and constitutional importance. However, the recordings of a case are usually very long which makes it both time and effort consuming for legal professionals to study the critical arguments in the legal deliberations. In this research, we summarise the second part of a combined research-industrial project for building an automated tool designed specifically to link segments in the text judgement to semantically relevant timespans in the videos of the hearings. The tool is employed as a User-Interface (UI) platform that provides a better access to justice by bookmarking the timespans in the videos which contributed to the final judgement of the case. We explain how we employ AI generative technology to retrieve the relevant links and show that the customisation of the GPT text embeddings to our dataset achieves the best accuracy for our automatic linking system.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.47</identifier>
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%0 Conference Proceedings
%T Automatic Linking of Judgements to UK Supreme Court Hearings
%A Saadany, Hadeel
%A Orasan, Constantin
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F saadany-orasan-2023-automatic
%X One the most important archived legal material in the UK is the Supreme Court published judgements and video recordings of court sittings for the decided cases. The impact of Supreme Court published material extends far beyond the parties involved in any given case as it provides landmark rulings on arguable points of law of the greatest public and constitutional importance. However, the recordings of a case are usually very long which makes it both time and effort consuming for legal professionals to study the critical arguments in the legal deliberations. In this research, we summarise the second part of a combined research-industrial project for building an automated tool designed specifically to link segments in the text judgement to semantically relevant timespans in the videos of the hearings. The tool is employed as a User-Interface (UI) platform that provides a better access to justice by bookmarking the timespans in the videos which contributed to the final judgement of the case. We explain how we employ AI generative technology to retrieve the relevant links and show that the customisation of the GPT text embeddings to our dataset achieves the best accuracy for our automatic linking system.
%R 10.18653/v1/2023.emnlp-industry.47
%U https://aclanthology.org/2023.emnlp-industry.47
%U https://doi.org/10.18653/v1/2023.emnlp-industry.47
%P 492-500

Markdown (Informal)

[Automatic Linking of Judgements to UK Supreme Court Hearings](https://aclanthology.org/2023.emnlp-industry.47) (Saadany & Orasan, EMNLP 2023)

• Automatic Linking of Judgements to UK Supreme Court Hearings (Saadany & Orasan, EMNLP 2023)

ACL

• Hadeel Saadany and Constantin Orasan. 2023. Automatic Linking of Judgements to UK Supreme Court Hearings. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 492–500, Singapore. Association for Computational Linguistics.