Syntactic Graph Convolutional Network for Spoken Language Understanding

Abstract

Slot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge. In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly. Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work. At last, we apply the BERT model to further improve the performance on both slot filling and intent detection. * The work was done when the first author was an intern at Meituan Group. The first two authors contribute equally.

STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API

“…Existing studies can be broadly classified into two categories: pipeline and end-to-end methods. The former is typically divided into several modules, including natural language understanding (NLU) [4][5][6], dialogue state tracking (DST) [7], dialogue policy (Policy) [8,9], and natural language generation (NLG) [10]. In contrast, end-to-end methods [11][12][13][14][15] build the system using a single model, which directly takes a natural language context as input and outputs a natural language response as well.…”