End-to-end task dependent recurrent entity network for goal-oriented dialog learning

In this paper, we introduce the Task Dependent Recurrent Entity Network (TDREN) to solve Dialogue System Technology Challenges 6 (DSTC 6) track 1. Traditionally, there have been methods such as collecting the intent of the user in a conversation directly using rules. We design an end-to-end structure that properly models the restaurant pre-related user preferences that appear in the dialogue and gives appropriate responses. We perform experiments on the TDREN and achieved 97.7% at precision 1. We propose a new artificial neural network structure and recurrent cell for modeling user preference information. Then, we show that task-oriented dialogue modeling experiment results using the structure and the recurrent cell.