  Recent graph-based models for multi-intent SLU 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
unidirectional guidance from intent to slot, while there are bidirectional
inter-correlations between intent and slot; (2) adopt 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 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 heterogeneous
graph attention networks working on the proposed two heterogeneous semantics
label graphs, which effectively represent the relations among the semantics
nodes and label nodes. Besides, we further propose Co-guiding-SCL Net, which
exploits the single-task and dual-task semantics contrastive relations. For the
first stage, we propose single-task supervised contrastive learning, and for
the second stage, we propose co-guiding supervised contrastive learning, which
considers the two tasks' mutual guidances in the contrastive learning
procedure. Experiment results on multi-intent SLU show that our model
outperforms existing models by a large margin, obtaining a relative improvement
of 21.3% over the previous best model on MixATIS dataset in overall accuracy.
We also evaluate our model on the zero-shot cross-lingual scenario and the
results show that our model can relatively improve the state-of-the-art model
by 33.5% on average in terms of overall accuracy for the total 9 languages.
