Multi Intent Spoken Language Understanding

Multi-intent spoken language understanding (SLU) focuses on enabling computers to accurately interpret user utterances containing multiple intentions, a crucial step for building robust and natural human-computer interaction systems. Current research emphasizes improving the joint modeling of intent detection and slot filling, often employing contrastive learning techniques and graph-based models to capture the complex relationships between different intents and their associated information. These advancements, including the exploration of large language models and end-to-end architectures, aim to enhance accuracy and efficiency, leading to more sophisticated and user-friendly applications in areas like virtual assistants and conversational AI.