Transfer Learning for Knowledge Acquisition in Domain-Specific Natural Language Processing Tasks

Abstract

Transfer learning has emerged as a powerful strategy for enhancing knowledge acquisition in domain-specific Natural Language Processing (NLP) applications. By leveraging models pre-trained on large-scale corpora, transfer learning facilitates the efficient adaptation of linguistic representations to specialized domains such as biomedical, legal, or technical fields. This approach has shown remarkable success in overcoming the limitations posed by scarce labeled data, enabling the extraction of nuanced domain-specific patterns that might otherwise remain undetected. Notably, the evolution of transformer-based architectures has accelerated breakthroughs in contextualized embeddings and has opened up opportunities for more sophisticated representation of domain-specific semantics.  In this paper, we investigate transfer learning methodologies tailored for domain-specific NLP tasks with an emphasis on practical strategies and theoretical underpinnings. We discuss fundamental principles that inform model pre-training, fine-tuning, and evaluation, as well as advanced techniques for injecting domain knowledge into large-scale language representations. We also explore how transfer learning can reduce the dependence on labeled data and expedite the development of accurate domain-specific systems. Finally, we analyze challenges and propose research directions to further enhance domain-specific NLP outcomes, with the aim of establishing a foundation for robust and efficient applications in real-world scenarios.