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

Abstract Text classification with hierarchical labels is a prevalent and challenging task in natural language processing. Examples include assigning ICD codes to patient records, tagging patents with IPC classes, assigning EUROVOC descriptors to European legal texts, and more. Despite the prevalence of hierarchical text classification problems, a comprehensive understanding of state-of-the-art methods across different application domains has been lacking. In this paper, we propose a unified methodology to break down the boundaries between these different domains, thus enabling cross-domain transfer of innovative ideas. We first construct a Unified Framework that translates distinct domain-specific methods into a common architectural language. Applying this framework, we conduct a comprehensive Cross-Domain Benchmark 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 .