A task-oriented neural dialogue system capable of knowledge accessing

As one of the most important AI applications, task-oriented dialogue systems have been a hot topic in this field. These systems are primarily designed to fulfill tasks and provide answers to queries, such as checking the weather or booking flights. However, there are two major challenges in implementing task-oriented dialogue systems: selecting the right knowledge from the relevant knowledge base and combining the knowledge to produce grammatically correct and fluent replies. In this paper, we present a task-oriented dialogue system using an end-to-end trainable neural model. This dialogue system consists of four components: an encoder for encoding the dialogue history, a three-hop memory network for storing the relevant knowledge entries and dialogue history words, a knowledge screener for selecting the knowledge that is really related to the dialogue history, and a decoder for generating the replies. Finally, we validate the proposed dialogue system on a benchmark dataset (KVRET). The experimental results show that the proposed model generates more accurate replies than baselines in automatic and human evaluations. Also, we verify the superiorities of the two improvements by ablation experiments.