Building a personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved in the template selection responses. However, preparing a massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on memory networks for response generation in a personalized task-oriented dialogue system. Our model consists of three parts: a retrieval module, a memory encoder network and a memory decoder network. Retrieval module employs the user utterances and user attributes to collect relevant responses from other users. Memory encoder is trained with textual features to obtain dialogue representation. Memory decoder is composed of an RNN and a rule-memory network for response generation. Experiments on the benchmark dataset show that our model achieves better performance than strong baselines in personalized task-oriented dialogue generation.

建立个性化的，面向任务的对话系统是一项重要但具有挑战性的任务。模板选择响应已取得重大成功。但是，准备大量的响应模板既耗时又费力。在本文中，我们提出了一个基于内存网络的端到端框架，用于在面向任务的个性化对话系统中生成响应。我们的模型包括三个部分：检索模块，内存编码器网络和内存解码器网络。检索模块利用用户话语和用户属性来收集其他用户的相关响应。内存编码器具有文本功能训练，以获取对话表示。内存解码器由RNN和规则内存网络组成，用于生成响应。