  Recent graph-based models for joint multiple intent detection and slot
filling have obtained promising results through modeling the guidance from the
prediction of intents to the decoding of slot filling. However, existing
methods (1) only model the \textit{unidirectional guidance} from intent to
slot; (2) adopt \textit{homogeneous graphs} to model the interactions between
the slot semantics nodes and intent label nodes, which limit the performance.
In this paper, we propose a novel model termed Co-guiding Net, which implements
a two-stage framework achieving the \textit{mutual guidances} between the two
tasks. In the first stage, the initial estimated labels of both tasks are
produced, and then they are leveraged in the second stage to model the mutual
guidances. Specifically, we propose two \textit{heterogeneous graph attention
networks} working on the proposed two \textit{heterogeneous semantics-label
graphs}, which effectively represent the relations among the semantics nodes
and label nodes. Experiment results show that our model outperforms existing
models by a large margin, obtaining a relative improvement of 19.3\% over the
previous best model on MixATIS dataset in overall accuracy.
