Intent Class

Intent classification research focuses on accurately categorizing user intents, particularly addressing the challenge of identifying novel or "open" intents not present in the training data. Current efforts concentrate on handling imbalanced datasets, improving the robustness of models to noisy or incomplete data through techniques like soft labeling and contrastive learning, and exploring alternative encoding methods beyond traditional one-hot representations to better capture the complexity of intent spaces. These advancements are crucial for building more robust and adaptable dialogue systems and other applications requiring accurate understanding of user needs.