  Recent joint multiple intent detection and slot filling models employ label
embeddings to achieve the semantics-label interactions. However, they treat all
labels and label embeddings as uncorrelated individuals, ignoring the
dependencies among them. Besides, they conduct the decoding for the two tasks
independently, without leveraging the correlations between them. Therefore, in
this paper, we first construct a Heterogeneous Label Graph (HLG) containing two
kinds of topologies: (1) statistical dependencies based on labels'
co-occurrence patterns and hierarchies in slot labels; (2) rich relations among
the label nodes. Then we propose a novel model termed ReLa-Net. It can capture
beneficial correlations among the labels from HLG. The label correlations are
leveraged to enhance semantic-label interactions. Moreover, we also propose the
label-aware inter-dependent decoding mechanism to further exploit the label
correlations for decoding. Experiment results show that our ReLa-Net
significantly outperforms previous models. Remarkably, ReLa-Net surpasses the
previous best model by over 20\% in terms of overall accuracy on MixATIS
dataset.
