Exploring advancements in deep learning for natural language processing tasks

Abstract

This literature survey explores the transformative influence of deep learning on Natural Language Processing (NLP), revealing a dynamic interplay between these fields. Deep learning techniques, characterized by neural network architectures, have propelled NLP into a realm where machines not only comprehend but also generate human language. The survey covers various NLP applications, such as sentiment analysis, machine translation, text summarization, question answering, and speech recognition, scasing significant strides attributed to deep learning models like Transformer, BERT, GPT, and attention-based Sequence-to-Sequence models. These advancements have redefined the landscape of NLP tasks, setting new benchmarks for performance. ever, challenges persist, including limited data availability in certain languages, increasing model sizes, and ethical considerations related to bias and fairness. Overcoming these hurdles requires innovative approaches for data scarcity, the development of computationally efficient models, and a focus on ethical practices in research and application. This survey provides a comprehensive overview of the progress and obstacles in integrating deep learning with NLP, offering a roadmap for navigating this evolving domain.