Intent Detection
Intent detection focuses on automatically identifying the user's goal or purpose from their input, whether text or speech, a crucial task for various applications like chatbots and personalized systems. Current research emphasizes improving accuracy and efficiency, particularly in handling out-of-domain queries and multiple simultaneous intents, often leveraging large language models (LLMs) and transformer-based architectures alongside traditional machine learning methods. This field is significant because accurate intent detection underpins the development of more robust and user-friendly human-computer interaction systems across diverse domains, from e-commerce to healthcare.
Papers
Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition
Xiaoshuai Song, Yutao Mou, Keqing He, Yueyan Qiu, Pei Wang, Weiran Xu
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference
Dejan Porjazovski, Yaroslav Getman, Tamás Grósz, Mikko Kurimo