A LegalRuleML editor with transformer-based autocompletion

Abstract: The construction industry has pursued automated compliance checking for decades, but legal requirements conveyed in natural language are not intended for machine processing. There have been numerous attempts to translate these requirements into computable representations, progressing from manual to fully-automated approaches. However, it is unclear if fully-automated translation will become reliable and interpretable enough for legal matters. We propose a LegalRuleML Editor with Transformer-based Autocompletion to facilitate a semi-automated workflow with minimal manual effort. A deep learning model generates initial translations and contextualised autocompletion options. This strategy offers experts a superior translation process, including continuous improvements approximating full automation.