Fine Grained Intent

Fine-grained intent classification focuses on identifying the precise, nuanced intentions behind text, going beyond simple topic categorization. Current research employs various approaches, including transformer-based models and clustering frameworks, often leveraging techniques like question-answering retrieval or two-stage pipelines combining high-level intent classification with fine-grained intent generation. This area is crucial for improving natural language understanding in applications like customer service chatbots, legal document analysis, and scientific literature revision, enabling more accurate and efficient processing of human communication.