  Profile-based intent detection and slot filling are important tasks aimed at
reducing the ambiguity in user utterances by leveraging user-specific
supporting profile information. However, research in these two tasks has not
been extensively explored. To fill this gap, we propose a joint model, namely
JPIS, designed to enhance profile-based intent detection and slot filling. JPIS
incorporates the supporting profile information into its encoder and introduces
a slot-to-intent attention mechanism to transfer slot information
representations to intent detection. Experimental results show that our JPIS
substantially outperforms previous profile-based models, establishing a new
state-of-the-art performance in overall accuracy on the Chinese benchmark
dataset ProSLU.
