Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling
This work addresses natural language understanding for dialogue systems, but it is incremental as it builds on existing multi-task learning approaches by adding syntactic and semantic dependencies.
The paper tackles the joint tasks of intent detection and slot filling by incorporating dependency structures and label semantics, achieving competitive performance on two public datasets.
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.