Transfer Learning in Natural Language Processing

Transfer learning has been a game-changer for natural language processing (NLP), and this technique has massively accelerated progress in the field of NLP, specifically by substantially benefiting from advancements being made elsewhere. We evaluated their performance economic model on a set of tasks and found they outperformed generic transformer models in accuracy as well as generalization. This is also a reminder of the importance of large-scale pretraining, which requires less task-specific data and consequently facilitates faster model development. To achieve a better understanding of the impact on model performance from fine-tuning, combined with datasets characteristics and architecture details. They demonstrated that transfer learning models not only increase classification accuracy in various fields, ranging from healthcare to legal document analysis but also vastly improve the efficiency. These results have important implications, they extend transfer learning from being merely a trend in NLP to something truly necessary for making progress on several fundamental task of NLP. However, work is not done on model interpretability and extending these improvements to low resource languages. This suggests future research in operationalizing these powerful tools transparently and fairly across a wide range of linguistic and cultural contexts. We hope that it can help, at least in some way, to grow the community interested on learning more about how these models work so effectively and guide them toward using transfer learning wherever valuable.

I. Introduction

The transfer learning (TL) has emerged as a cornerstone of NLP, enabling the development and deployment of new kinds of language models. A good NLP model had to be trained on very rich task specific data, which is both time consuming and resource intensive. Transfer learning addresses these problems by enabling pre-trained models to be fine-tuned on new tasks with only a few data, which in turn cuts down the time and culmination costs of the training stage (Salehi et al., 2023). I could say that not only has this enabled the development of state-of-the-art performance in domains with limited labeled data.