  Slot filling and intent detection are two fundamental tasks in the field of
natural language understanding. Due to the strong correlation between these two
tasks, previous studies make efforts on modeling them with multi-task learning
or designing feature interaction modules to improve the performance of each
task. However, none of the existing approaches consider the relevance between
the structural information of sentences and the label semantics of two tasks.
The intent and semantic components of a utterance are dependent on the
syntactic elements of a sentence. In this paper, we investigate a multi-grained
label refinement network, which utilizes dependency structures and label
semantic embeddings. Considering to enhance syntactic representations, we
introduce the dependency structures of sentences into our model by graph
attention layer. To capture the semantic dependency between the syntactic
information and task labels, we combine the task specific features with
corresponding label embeddings by attention mechanism. The experimental results
demonstrate that our model achieves the competitive performance on two public
datasets.
