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.

本文提出一种基于依存结构和标签语义嵌入的多粒度标签精炼网络，利用图注意层介绍了句子的依存结构来增强句法表示，通过注意机制将任务特定的特征与相应的标签嵌入相结合捕获语法信息和任务标签之间的语义依赖关系。在两个公共数据集上的实验结果表明，我们的模型取得了竞争性能。