The Evolution of Intent Detection: Deep Learning-Based Paradigms for Natural Language Understanding

Intent detection plays a pivotal role in natural language understanding, enabling systems to accurately interpret user goals from text or speech inputs - a capability essential to virtual assistants, chatbots, and conversational agents. This survey presents a comprehensive analysis of intent detection methods spanning traditional machine learning models, deep learning architectures, such as convolutional and recurrent networks, and advanced transformer-based frameworks. It examines a wide range of datasets across text, voice, and multilingual modalities to reflect diverse application scenarios. The discussion includes evaluation strategies for classification, multilabel detection, and sequence-based tasks, offering insights into the strengths and limitations of each approach. Key challenges addressed include multi-intent prediction, handling domain shifts in low-resource settings, and maintaining robustness under real-time constraints. The study identifies a clear shift toward transformer-driven and instruction-tuned models, with increased focus on self-supervised learning, federated systems, and continual adaptation. By consolidating architectural progress, benchmark analysis, and deployment considerations, this survey provides a unified reference for advancing intent detection research in both academic and industrial contexts.