Multi-Domain Benchmarking Frameworks for Comparative Analysis of Neural Language Processing Architectures

Multi-domain benchmarking frameworks are essential for systematically comparing neural language processing architectures across diverse linguistic environments. They enable researchers to evaluate model robustness, adaptability, and generalization beyond single-domain applications. Existing methods often rely on single-domain benchmarks or isolated datasets, which fail to capture the complexities of real-world text processing. As a result, architectures may appear effective in constrained settings but perform inconsistently when exposed to new domains with different terminologies, structures, and contexts. To address these challenges, this study proposes the Cross-Domain Transfer Benchmarking (CDTB) framework. CDTB evaluates models by training on a source domain and systematically testing across multiple target domains, measuring cross-domain transferability, resilience to domain shift, and adaptation efficiency. The proposed framework is applied in multilingual healthcare chatbot systems, where reliable performance across medical, conversational, and cultural contexts is critical. CDTB ensures fair comparison of architectures in handling varied linguistic patterns and domain-specific demands. Findings reveal that CDTB effectively highlights architectures with superior generalization, enabling more informed model selection. This outcome contributes to the development of robust NLP systems that can function across domains with greater accuracy, reliability, and fairness.