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.

本研究提出了一种基于异构标签图的模型（HLG），以捕捉标签之间的依赖关系和关联关系，并利用这些关系来提高多重意图检测和位置填充任务的性能。我们还提出了标签感知的相互依赖解码机制，以进一步利用标签之间的关联关系来解码。实验结果表明，我们的 ReLa-Net 模型在 MixATIS 数据集上的整体准确性比以前的最佳模型高出 20％以上。