Deep Learning Approaches for Natural Language Understanding

This paper presents a comprehensive survey of deep learning approaches for natural language understanding (NLU). We review state-of-the-art models including transformers, BERT variants, and GPT architectures. Our analysis covers key tasks such as sentiment analysis, named entity recognition, question answering, and text summarization. We provide benchmarks across multiple datasets and discuss the trade-offs between model complexity and performance. The paper also addresses challenges in multilingual NLU and low-resource settings.