A spoken language understanding (SLU) system includes two main tasks, slot
filling (SF) and intent detection (ID). The joint model for the two tasks is
becoming a tendency in SLU. But the bi-directional interrelated connections
between the intent and slots are not established in the existing joint models.
In this paper, we propose a novel bi-directional interrelated model for joint
intent detection and slot filling. We introduce an SF-ID network to establish
direct connections for the two tasks to help them promote each other mutually.
Besides, we design an entirely new iteration mechanism inside the SF-ID network
to enhance the bi-directional interrelated connections. The experimental
results show that the relative improvement in the sentence-level semantic frame
accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets,
respectively, compared to the state-of-the-art model.

本文提出了一种新颖的双向互相关联的联合意图检测和槽填充模型，通过引入 SF-ID 网络和全新的迭代机制来增强两个任务之间的双向关联，实验结果表明，在 ATIS 和 Snips 数据集上，与现有最先进的模型相比，本模型在句子级语义框架准确度上相对提高了 3.79% 和 5.42%。