Abstract
End-to-end dialog systems have become very popular because they hold the promise of learning directly from human to human dialog interaction. Retrieval and Generative methods have been explored in this area with mixed results. A key element that is missing so far, is the incorporation of a-priori knowledge about the task at hand. This knowledge may exist in the form of structured or unstructured information. As a first step towards this direction, we present a novel approach, Knowledge based end-to-end memory networks (KB-memN2N), which allows special handling of named entities for goal-oriented dialog tasks. We present results on two datasets, DSTC6 challenge dataset and dialog bAbI tasks.
Abstract (translated)
端到端的对话系统已经变得非常流行，因为它们承诺直接从人类对话交互学习。已经在这个领域探索了检索和生成方法，结果不一。迄今为止缺失的一个关键因素是将关于手头任务的先验知识合并在一起。这些知识可以以结构化或非结构化信息的形式存在。作为朝这个方向迈出的第一步，我们提出了一种新颖的方法，基于知识的端到端内存网络（KB-memN2N），它允许特殊处理命名实体以执行面向目标的对话任务。我们在两个数据集上呈现结果，DSTC6挑战数据集和对话bAbI任务。
URL
https://arxiv.org/abs/1804.08204
