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Profile-based intent detection and slot filling are important tasks aimed atreducing the ambiguity in user utterances by leveraging user-specificsupporting profile information. However, research in these two tasks has notbeen extensively explored. To fill this gap, we propose a joint model, namelyJPIS, designed to enhance profile-based intent detection and slot filling. JPISincorporates the supporting profile information into its encoder and introducesa slot-to-intent attention mechanism to transfer slot informationrepresentations to intent detection. Experimental results show that our JPISsubstantially outperforms previous profile-based models, establishing a newstate-of-the-art performance in overall accuracy on the Chinese benchmarkdataset ProSLU.

JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention