The main goal of modelling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios. End-to-end trained neural dialog systems are an important line of research for such generalized dialog models as they do not resort to any situation-specific handcrafting of rules. Modelling personalization of conversation in such agents is important for them to be truly 'smart' and to integrate seamlessly into the lives of human beings. However, the topic has been largely unexplored by researchers as there are no existing corpora for training dialog systems on conversations that are influenced by the profiles of the speakers involved. In this paper, we present a new dataset of goal-oriented dialogs with profiles attached to them. We also introduce a framework for analyzing how systems model personalization in addition to performing the task associated with each dialog. Although no existing model was able to sufficiently solve our tasks, we provide baselines using a variety of learning methods and investigate in detail the shortcomings of an end-to-end dialog system based on Memory Networks. Our dataset and experimental code are publicly available at https://github.com/chaitjo/personalized-dialog

本文提出了一种基于个人资料的目标导向对话数据集，结合修改内在神经系统结构、使用多任务学习的方法，将个性化应用于对话系统。

目标导向对话中的个性化