We investigate automated rule extraction from building regulations using a neural semantic parser. This task is regarded as a main requirement to enable automated compliance checking in the built en-vironment. The performance of deep learning models is strongly dependent on the quantity and quality of the training data and the task complexity, which is particularly relevant for domain-specific tasks with limited da-ta and domain-specific terminology.