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Lessons learned building a legal inference dataset

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Abstract

Legal inference is fundamental for building and verifying hypotheses in police investigations. In this study, we build a Natural Language Inference dataset in Korean for the legal domain, focusing on criminal court verdicts. We developed an adversarial hypothesis collection tool that can challenge the annotators and give us a deep understanding of the data, and a hypothesis network construction tool with visualized graphs to show a use case scenario of the developed model. The data is augmented using a combination of Easy Data Augmentation approaches and round-trip translation, as crowd-sourcing might not be an option for datasets with sensible data. We extensively discuss challenges we have encountered, such as the annotator’s limited domain knowledge, issues in the data augmentation process, problems with handling long contexts and suggest possible solutions to the issues. Our work shows that creating legal inference datasets with limited resources is feasible and proposes further research in this area.

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Data availability

The data is available on GitHub (https://github.com/onspark/LEAP_NLI_v2.0).

Notes

  1. Both police manuals for creating investigation result reports and investigation review reports were internal documents. We pursued expert interviews and solicited detailed explanations in written form to understand these processes more accurately.

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Acknowledgements

This research was supported and funded by the Korean National Police Agency [Project Name: AI-Based Crime Investigation Support System/ Project Number: PR10-02-000-21]. The authors also thank the Legal Informatics and Forensic Science (LIFS) institute at Hallym University and its researchers for their indispensable help in creating the data.

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Authors and Affiliations

  1. Korea University School of Cybersecurity & HM Company, Seoul, South Korea

    Sungmi Park

  2. DFIR Science, LLC, Champaign, IL, USA

    Joshua I. James

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  1. Sungmi Park
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  2. Joshua I. James
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Correspondence to Joshua I. James.

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Appendix 1

Appendix 1

See Table 13. Key phrases are highlighted in bold.

Table 13 Types of inference marked with an asterisk (*) were adapted from (Nie et al. 2020)
Full size table

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Park, S., James, J.I. Lessons learned building a legal inference dataset. Artif Intell Law 32, 1011–1044 (2024). https://doi.org/10.1007/s10506-023-09370-x

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