Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling

Abstract

Intention detection and slot filling are two major subtasks in building a spoken language understanding (SLU) system. These two tasks are closely related to each other, and information from one will influence the other, establishing a bidirectional contributory relationship. Existing studies have typically modeled the two-way connection between these two tasks simultaneously in a unified framework. However, these studies have merely contributed to the research direction of fully using the correlations between feature information of the two tasks, without sufficient focusing on and utilizing native textual semantics. In this article, we propose a semantic guidance (SG) framework, enabling enhancing the understanding of textual semantics by dynamically gating the information from both tasks to acquire semantic features, ultimately leading to higher joint task accuracy. Experimental results on two widely used public datasets show that our model achieves state-of-the-art performance.