# CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking

arXiv: https://arxiv.org/abs/2403.02231 · HTML: https://mlsanity.com/paper/2403.02231/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generation](https://mlsanity.com/paper/2505.19804/) (2025)
- [Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models](https://mlsanity.com/paper/2509.17283/) (2025)
- [Representing Normative Regulations in OWL DL for Automated Compliance Checking Supported by Text Annotation](https://mlsanity.com/paper/2504.05951/) (2025)
- [Towards an automated AI-based framework for floor plan compliance checks for residential buildings](https://mlsanity.com/paper/2607.00015/) (2026)
- [ARCHER: Agentic Rule and Compliance Harness for Executable Regulations](https://mlsanity.com/paper/2607.25566/) (2026)
- [Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents](https://mlsanity.com/paper/2608.06312/) (2026)

## Abstract (arXiv)

Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland. Only the self-contained sentences, which express complete rules without needing additional context, were considered as they are essential for ACC. Each sentence was manually annotated with entities and relations by a team of 12 annotators to facilitate machine-readable rule generation, followed by careful curation to ensure accuracy. The final dataset comprises 4,297 entities and 4,329 relations across various categories, serving as a robust ground truth. CODE-ACCORD supports a range of ML and Natural Language Processing (NLP) tasks, including text classification, entity recognition, and relation extraction. It enables applying recent trends, such as deep neural networks and large language models, to ACC.