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.

翻译：空格填充和意图探测是自然语言理解领域的两个基本任务。由于这两个任务之间有着密切的关联性,以前的研究努力用多任务学习或设计特征互动模块来模拟这两个任务,以改进每项任务的业绩。然而,现有的方法没有一个考虑到句子的结构信息与两种任务标签语义的关联性。一个词句的意图和语义组成部分取决于一个句子的合成元素。在本文件中,我们调查一个多级标签精细网络,利用依赖结构和标签语义嵌入。考虑到加强合成表达方式,我们通过图形注意层将句句的依赖性结构引入我们的模型。要捕捉语义信息和任务标签之间的语义依赖性,我们将任务的具体特征与相应的标签嵌入注意机制结合起来。实验结果显示,我们的模型在两个公共数据集上取得了竞争性的性能。