  Multi-intent detection and slot filling joint models are gaining increasing
traction since they are closer to complicated real-world scenarios. However,
existing approaches (1) focus on identifying implicit correlations between
utterances and one-hot encoded labels in both tasks while ignoring explicit
label characteristics; (2) directly incorporate multi-intent information for
each token, which could lead to incorrect slot prediction due to the
introduction of irrelevant intent. In this paper, we propose a framework termed
DGIF, which first leverages the semantic information of labels to give the
model additional signals and enriched priors. Then, a multi-grain interactive
graph is constructed to model correlations between intents and slots.
Specifically, we propose a novel approach to construct the interactive graph
based on the injection of label semantics, which can automatically update the
graph to better alleviate error propagation. Experimental results show that our
framework significantly outperforms existing approaches, obtaining a relative
improvement of 13.7% over the previous best model on the MixATIS dataset in
overall accuracy.
