Despite the successes and fast improvements in artificial intelligence, the interpretation of legal documents is still a highly manual process. Because of ethical considerations and the missing interpretability of modern deep learning models, to date, the preferred automation methodology is to translate the normative documents into a logic representation, which allows execution by an automated theorem prover. Because regulations are drafted in natural language and frequently amended, an automated or semi-automated translation process is required.
An end-to-end deep learning translation model was trained that allows experts to translate regulation clauses into LegalRuleML, the representation format selected for this study. A user interface to interact with this transformer-based deep learning model was developed to provide the user with full translations and auto-completion options. This project will investigate more user-friendly visual languages to represent the regulation clauses.
In this context, the following two aspects need consideration:
1. Can the textual representation be mapped into a visual language?
2. How can the auto-completion functionality be integrated into a visual editor?
In addition to developing a modern user interface, this project lets you experience direct interaction with state-of-the-art deep learning models and modify them where required to provide the best possible user experience.
Undergraduate
A visual editor for the translation of regulation clauses to LegalRuleML. The editor has the functionality of proposing a full LegalRuleML translation, as well as providing context-dependent auto-completion options.
SOFTENG 350 would be useful
Computer Science (303S.499, Lab)