JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention
This addresses ambiguity in user utterances for natural language processing applications, but it is incremental as it builds on existing profile-based models.
The paper tackled profile-based intent detection and slot filling by proposing JPIS, a joint model that incorporates profile information and a slot-to-intent attention mechanism, achieving new state-of-the-art overall accuracy on the ProSLU dataset.
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.