Research progress and challenges of deep learning in Natural Language Processing
Yuhan Fan · Advances in Engineering Innovation · 2025
With the rapid development of artificial intelligence, Natural Language Processing (NLP) has emerged as a critical area for enabling intelligent human-computer interaction. This paper reviews key deep learning technologies and their applications in NLP. It first examines foundational techniques such as word embeddings and pre-trained models, and analyzes the structures and use cases of core models includingConvolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs) and their variants, as well as Transformers. It then explores the application of these models in tasks such as sentiment analysis, machine translation, and question-answering systems. The study highlights how pre-trained models like BERT and GPT significantly enhance semantic understanding through large-scale unsupervised learning. However, challenges remain, including limited interpretability, weak performance in low-resource languages, and inadequate multimodal integration. The paper concludes by discussing future directions such as lightweight model design, cross-lingual transfer learning, and deep multimodal fusion. This research aims to provide theoretical references for advancing NLP technology and enhancing its practicality across various domains.