Exploring the Frontiers of Transfer Learning in NLP: an In-Depth Survey and Analysis

EasyChair Preprint no. 11945

Abstract

Transfer learning has emerged as a pivotal paradigm in Natural Language Processing (NLP), revolutionizing the way models are trained and applied. This comprehensive survey delves into the frontiers of transfer learning in NLP, presenting an in-depth analysis of the latest advancements, methodologies, and challenges. From pre-trained language models to domain adaptation techniques, we explore the diverse landscape of transfer learning, providing insights into its applications, benefits, and future directions. Through an exhaustive review of key literature, we aim to offer a nuanced understanding of the state-of-the-art in transfer learning for NLP and its potential impact on various NLP tasks.

Keyphrases: Domain Adaptation, fine-tuning, Named Entity Recognition, Natural Language Processing, neural networks, NLP applications, Pre-trained Language Models, Sentiment Analysis, text classification, Transfer Learning

@Booklet{EasyChair:11945,
  author = {Asad Ali and Virat Koli},
  title = {Exploring the Frontiers of Transfer Learning in NLP: an In-Depth Survey and Analysis},
  howpublished = {EasyChair Preprint no. 11945},

  year = {EasyChair, 2024}}