Combining a Legal Knowledge Model with Machine Learning for Reasoning with Legal Cases

Mumford, Jack ORCID: 0000-0003-2467-5785, Atkinson, Katie ORCID: 0000-0002-5683-4106 and Bench-Capon, Trevor ORCID: 0000-0003-3975-4398 (2023) Combining a Legal Knowledge Model with Machine Learning for Reasoning with Legal Cases. In: ICAIL 2023: Nineteenth International Conference on Artificial Intelligence and Law, 2023-6-19 - 2023-6-23, Braga, Portugal.

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Abstract

Recent years have witnessed significant progress in the deployment of advanced Natural Language Processing (NLP) techniques based on transformer technology, across many domains and applications. However, in legal domains, due to the complexity, length, and sparsity of legal case documents, the use of these advanced NLP techniques has offered comparatively slight returns. Perhaps even more importantly, such methods are critically lacking in explainability and justification of outputs, which are essential for many legal applications. We propose that the direction of these NLP techniques should be aimed at ascription to a legal knowledge model, which can then provide the necessary and auditable justifications for the rationale of any case outcome. In this paper we investigate the effectiveness of using Hierarchical Bidirectional Encoder Representations from Transformers (H-BERT) models to ascribe to an Angelic Domain Model (ADM) that is able to represent the legal knowledge of a domain in a structured way, enabling justifications and improving performance. Our study involved an annotation task on a popular domain, cases from the European Court of Human Rights, to gain an understanding of the balance of complaints in the domain. The data set produced from this study enabled training of models for factor ascription using the classification targets derived from the annotations. We present results of experiments conducted to evaluate the performance of the ascription task at three different levels of abstraction within the structured model.

Item Type:	Conference or Workshop Item (Unspecified)
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	17 May 2023 08:11
Last Modified:	27 Apr 2024 14:44
DOI:	10.1145/3594536.3595158
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170399