Extended Hybrid Code Networks for DSTC6 FAIR Dialog Dataset

Abstract

Goal-oriented dialog systems require a different approach compared to chit-chat conversations in that they should perform various subtasks as well as the dialog itself. Since the systems typically interact with an external database, it is efficient to import simple domain knowledge in order to deal with the external knowledge changes. This paper presents extended hybrid code networks for sixth dialog system technology challenge (DSTC6) Facebook AI research (FAIR) dialog dataset. Compared to the original hybrid code networks (HCNs), we reduced the required hand-coded rules and added trainable submodules. Due to the additional learning components and reasonable domain-specific rules, the proposed model can be applied to more complex domains and achieved 100% accuracies for all test sets.