Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification
This work addresses fine-grained hierarchical classification in regulatory-intensive domains such as customs tariff codes and export controls, where predictions must strictly adhere to hierarchical structures and rule-based boundaries. Existing approaches struggle to simultaneously ensure hierarchical validity, rule consistency, and boundary-aware reasoning. To bridge this gap, the paper formalizes, for the first time, a regulation-driven hierarchical classification task and introduces a constraint-aware hierarchical search framework. The method parses regulatory documents into a searchable tree and, at each step, retrieves only legally permissible candidate nodes, guiding path decisions through structured fields and evidential text snippets. Evaluated on four expert-validated datasets, the approach significantly outperforms baselines in average accuracy—particularly excelling in distinguishing adjacent categories and handling boundary cases—while producing auditable and traceable decision paths.