A BiRGAT Model for Multi-intent Spoken Language Understanding with
  Hierarchical Semantic Frames
Hongshen Xu Ruisheng Cao Su Zhu Sheng Jiang Hanchong Zhang Lu Chen Kai Yu
Previous work on spoken language understanding (SLU) mainly focuses on
single-intent settings, where each input utterance merely contains one user
intent. This configuration significantly limits the surface form of user
utterances and the capacity of output semantics. In this work, we first propose
a Multi-Intent dataset which is collected from a realistic in-Vehicle dialogue
System, called MIVS. The target semantic frame is organized in a 3-layer
hierarchical structure to tackle the alignment and assignment problems in
multi-intent cases. Accordingly, we devise a BiRGAT model to encode the
hierarchy of ontology items, the backbone of which is a dual relational graph
attention network. Coupled with the 3-way pointer-generator decoder, our method
outperforms traditional sequence labeling and classification-based schemes by a
large margin.