Deep Learning Techniques for Natural Language Processing: Recent Developments

EasyChair Preprint no. 12338

Abstract

Natural Language Processing (NLP) is a rapidly evolving field with a wide range of applications, from machine translation to sentiment analysis and question answering. Deep learning techniques have played a crucial role in advancing the state-of-the-art in NLP tasks, allowing models to learn complex patterns and representations directly from data. In this paper, we review recent developments in deep learning techniques for NLP, focusing on key advancements in areas such as neural network architectures, pretraining methods, and fine-tuning strategies. We discuss the rise of transformer-based models, such as BERT, and GPT, and their variants, which have achieved remarkable performance across a range of NLP tasks. We also explore techniques for handling challenges such as data scarcity, domain adaptation, and multilingual processing. Finally, we highlight promising directions for future research in deep learning for NLP, including the integration of symbolic knowledge, the development of more efficient models, and the exploration of multimodal approaches. Overall, deep learning has significantly advanced the capabilities of NLP systems, paving the way for more accurate, flexible, and scalable language understanding technologies.

Keyphrases: deep learning, Natural Language Processing (NLP), neural networks

@Booklet{EasyChair:12338,
  author = {Jane Elsa and Jeff Koraye},
  title = {Deep Learning Techniques for Natural Language Processing: Recent Developments},
  howpublished = {EasyChair Preprint no. 12338},

  year = {EasyChair, 2024}}