Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification

that exposes architectural gaps often overlooked in single-domain studies. We then demonstrate the framework's practical utility through a validation case study, where we synthesize a new state-of-the-art hierarchical text classification method by combining submodules that were developed for the medical and legal domains. Our extensive empirical analysis yields key insights and guidelines, confirming the necessity of cross-domain learning for designing effective methods. Our code and datasets are publicly available at https://github.com/aida-ugent/cross-domain-HTC.