Fg2seq: Effectively Encoding Knowledge for End-To-End Task-Oriented Dialog
Zhenhao He, Yuhong He, Qingyao Wu, Jian Chen
Abstract
End-to-end Task-oriented spoken dialog systems typically require modeling two types of inputs, namely, the dialog history which is a sequence of utterances and the knowledge base (KB) associated with the dialog history. While modeling these inputs, current state-of-the-art models typically ignore the rich structure in the knowledge graph or its intrinsic association with the dialog history. In this paper, we propose a Flow-to-Graph seq2seq model (FG2Seq) which can effectively encode knowledge by considering inherent structural information of the knowledge graph and latent semantic information from dialog history. Experiments on two publicly available task oriented dialog datasets show that our proposed FG2Seq achieves robust performance on generating appropriate system responses and outperforms the baseline systems.