Multidimensional Transfer Learning in Natural Language Processing: Domain, Task, Language, and Expertise Adaptation
Year: 2026
Type: dissertation
Abstract: Transfer learning in Natural Language Processing (NLP) mirrors a fundamental human cognitive ability: applying prior knowledge to learn new skills efficiently. Rather than training models from scratch for every new task, domain, or language, transfer learning enables systems to use knowledge from previous learning experiences. This capability is essential for addressing two critical challenges in modern NLP: the scarcity of labeled data affecting the vast majority of the world’s languages and specialized domains, and the prohibitive computational cost of training large-scale models from scratch. This dissertation presents a comprehensive investigation of transfer learning in NLP, organized around two distinct phases. The first phase, Adaptive Transfer Learning, addresses horizontal knowledge transfer across contexts through three research directions: unsupervised domain adaptation, where models must transfer across data distributions without target labels; task adaptation for structured prediction, where multiple interdependent tasks such as joint information extraction must be learned and transferred simultaneously across domains; and cross-lingual transfer, where knowledge from high-resource languages is shared to low-resource languages across typologically diverse language families. The second phase, Specialization Transfer Learning, addresses the emerging challenge of vertically refining general-purpose large language models for reliable deployment in high-stakes expert domains. This phase focuses on developing comprehensive evaluation frameworks for Retrieval-Augmented Generation systems in the medical domain, assessing critical dimensions beyond standard accuracy such as knowledge sufficiency, multi-document integration, and robustness to noisy or misleading retrieved content. Together, these contributions advance the understanding of how NLP models can be systematically transferred and specialized across domains, tasks, languages, and levels of expertise, spanning the full spectrum from data-limited to data-engineered paradigms. This dissertation includes both previously published and co-authored material.
Author: Ngo, Nghia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165425966