  Spoken Language Understanding (SLU), including intent detection and slot
filling, is a core component in human-computer interaction. The natural
attributes of the relationship among the two subtasks make higher requirements
on fine-grained feature interaction, i.e., the token-level intent features and
slot features. Previous works mainly focus on jointly modeling the relationship
between the two subtasks with attention-based models, while ignoring the
exploration of attention order. In this paper, we propose to replace the
conventional attention with our proposed Bilinear attention block and show that
the introduced Higher-order Attention Network (HAN) brings improvement for the
SLU task. Importantly, we conduct wide analysis to explore the effectiveness
brought from the higher-order attention.
