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.

本研究主要关注多意图的口语理解，提出了一个多意图数据集，使用 BiRGAT 模型编码本体项的层级结构，并配合三路指针生成器解码器，在实验中取得了显著优于传统序列标签和基于分类的方案的结果。