Training Millions of Personalized Dialogue Agents

Abstract

Current dialogue systems fail at being engaging for users, especially when trained endto-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-toend dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in (Zhang et al., 2018) is synthetic and only contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from (Zhang et al., 2018) and achieving state-of-the-art results.