Current dialogue systems are not very engaging for users, especially when
trained end-to-end without relying on proactive reengaging scripted strategies.
Zhang et al. (2018) showed that the engagement level of end-to-end 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 of limited size as it 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.

本文介绍了一个包含 500 万个人物角色和 7 亿个依据人物角色的对话的新数据集，研究表明，使用此数据集训练对话系统，尤其是当基于文本人物角色时，可以提高其表现，并且在 Zhang 等人（2018）的数据上进行微调可以实现最新的成果。