Intermediate representations to improve the semantic parsing of building regulations

Recent developments show that large transformer-based language models have the capability to generate coherent text and source code in response to user prompts. This capability can be used in the construction domain to interpret building regulations and convert them into a formal representation usable for automated compliance checking. While base-size models can already be taught to perform semantic parsing with decent quality, this paper shows how Intermediate Representations (IRs) can be used to improve the semantic parsing quality. With reversible IRs, the training time was reduced to almost a quarter of the initial duration, and through adding a hierarchical parsing step, improvements of up to 6.6% on F1 scores were reached. Furthermore, intermediate representations provide a novel and interpretable method towards a human-in-the-loop approach for translating building regulations into a formal representation.