Inventive Meta-Learning Strategies for Cross-Domain Natural Language Understanding in Distributed Knowledge Bases

Abstract

Natural Language Understanding (NLU) addresses cross-domain challenges in understanding language due to inconsistent data distributions, uncertain concept semantics, and inconsistent language use specific to individual domains across different geographical locations. The majority of classical deep learning systems perform poorly because it cannot generalize, leading to cross-domain underperformance in practical settings. Therefore, this research develops a new class of meta-learning that provides a systematic means to advance cross-domain adaptability for NLU in the context of distributed knowledge systems. More specifically, this approach combines federated learning methods, model-agnostic meta-learning (MAML), and domain-adaptive transformers for the first time to enable rapid extraction of domain-invariant representations while concurrently maintaining local contextual semantics across reasonably distributed datasets. The author conducted benchmark assessments on the most recent multi-domain evaluations and cross-lingual and domain-shifted data to simulate distributed knowledge systems. The custom model outperformed all baselines, transformers, and fine-tuning models by an average of 12.8% cross-domain accuracy, 15.3% lower generalization error, and 18.6% faster convergence rate. In the scenario, the new model decreased to less than 90% and maintained over 90% of its performance after a 40% reduction in training samples. Meta-learning enables superior transfer efficiency and model adaptation to new, previously unseen domains with minimal training. The new meta-learning frameworks successfully addressed the required performance and adaptability for advanced intelligent information systems for cross-domain NLU in distributed knowledge bases.