Natural Language Processing (NLP) has experienced a significant boost in
performance in recent years due to the emergence of transfer learning techniques.
Transfer learning is the process of leveraging pre-trained models on large amounts of
data and transferring the knowledge to downstream tasks with limited labelled data.
This paper presents a comprehensive review of the recent developments in transfer
learning for NLP. It also discusses the key concepts and architectures of transfer
learning, including fine-tuning, multi-task learning, and domain adaptation. The paper
also highlights the challenges of transfer learning and provides insights into future
research directions. The analysis presented here has significantly improved the
performance of NLP tasks, particularly in tasks with limited labelled data. Furthermore,
pre-trained language models such as BERT and GPT-3 have achieved state-of-the-art
performance in various NLP tasks, demonstrating the power of transfer learning in
NLP. Overall, this paper provides a comprehensive overview of the recent
developments in transfer learning for NLP and highlights the potential for future
advancements in the field. However, the challenges of domain adaptation and dataset
biases still need to be addressed to improve the generalization ability of transfer
learning models. The analysis also leaves room to investigate transfer learning in lowresource languages and to develop transfer learning techniques for speech and
multimodal NLP tasks.
Keywords: GPT, Multimodal NLP tasks, Natural learning processing, Pre-trained models transfer learning.
