Recent Advances in Transfer Learning for Natural Language Processing (NLP)

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.