A Typed Iteration Approach for Spoken Language Understanding

Abstract

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded information of the two subtasks to realize mutual interaction, nor can they achieve the bidirectional connection between them. In this paper, we propose a typed abstraction mechanism to enhance the performance of intent detection by utilizing the encoded information of SF tasks. In addition, we design a typed iteration approach, which can achieve the bidirectional connection of the encoded information and mitigate the negative effects of error propagation. The experimental results on two public datasets ATIS and SNIPS present the superiority of our proposed approach over other baseline methods, indicating the effectiveness of the typed iteration approach.