Teaching machines to accomplish tasks by conversing naturally with humans is
challenging. Currently, developing task-oriented dialogue systems requires
creating multiple components and typically this involves either a large amount
of handcrafting, or acquiring costly labelled datasets to solve a statistical
learning problem for each component. In this work we introduce a neural
network-based text-in, text-out end-to-end trainable goal-oriented dialogue
system along with a new way of collecting dialogue data based on a novel
pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue
systems easily and without making too many assumptions about the task at hand.
The results show that the model can converse with human subjects naturally
whilst helping them to accomplish tasks in a restaurant search domain.

本文介绍一种基于神经网络的端到端可训练的目标导向对话系统，以及一种新的数据收集方法，该方法基于一种新型的管道式 Wizard-of-Oz 框架。该方法可用于开发对话系统，帮助用户在餐厅搜索领域自然地交流并完成任务。