The personalized dialogue explores the consistent relationship between
dialogue generation and personality. Existing personalized dialogue agents
model persona profiles from three resources: sparse or dense persona
descriptions and dialogue histories. However, sparse structured persona
attributes are explicit but uninformative, dense persona texts contain rich
persona descriptions with much noise, and dialogue history query is both noisy
and uninformative for persona modeling. In this work, we combine the advantages
of the three resources to obtain a richer and more accurate persona. We design
a Contrastive Latent Variable-based model (CLV) that clusters the dense persona
descriptions into sparse categories, which are combined with the history query
to generate personalized responses. Experimental results on Chinese and English
datasets demonstrate our model's superiority in personalization.

本研究通过使用对比潜变量模型来将浓缩的用户描述信息聚类到稀疏的分类中，并与对话历史查询相结合，以生成更具个性化的响应。实验结果表明，该模型在个性化方面的优势在中英文数据集上都得到了证实。