  Personalized dialogue systems explore the problem of generating responses
that are consistent with the user's personality, which has raised much
attention in recent years. Existing personalized dialogue systems have tried to
extract user profiles from dialogue history to guide personalized response
generation. Since the dialogue history is usually long and noisy, most existing
methods truncate the dialogue history to model the user's personality. Such
methods can generate some personalized responses, but a large part of dialogue
history is wasted, leading to sub-optimal performance of personalized response
generation. In this work, we propose to refine the user dialogue history on a
large scale, based on which we can handle more dialogue history and obtain more
abundant and accurate persona information. Specifically, we design an MSP model
which consists of three personal information refiners and a personalized
response generator. With these multi-level refiners, we can sparsely extract
the most valuable information (tokens) from the dialogue history and leverage
other similar users' data to enhance personalization. Experimental results on
two real-world datasets demonstrate the superiority of our model in generating
more informative and personalized responses.
