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

TL;DR

This paper introduces JPIS, a joint model that leverages user profiles and a novel slot-to-intent attention mechanism to improve intent detection and slot filling, achieving state-of-the-art results on a Chinese benchmark dataset.

Contribution

The paper presents a new joint model, JPIS, that effectively integrates profile information and slot-to-intent attention for enhanced performance in intent detection and slot filling.

Findings

JPIS outperforms previous models on ProSLU dataset.

The slot-to-intent attention mechanism improves intent detection accuracy.

Profile integration enhances slot filling performance.

Abstract

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.