PersonaPKT: Building Personalized Dialogue Agents via
  Parameter-efficient Knowledge Transfer
Xu Han Bin Guo Yoon Jung Benjamin Yao Yu Zhang Xiaohu Liu Chenlei Guo
Personalized dialogue agents (DAs) powered by large pre-trained language
models (PLMs) often rely on explicit persona descriptions to maintain
personality consistency. However, such descriptions may not always be available
or may pose privacy concerns. To tackle this bottleneck, we introduce
PersonaPKT, a lightweight transfer learning approach that can build
persona-consistent dialogue models without explicit persona descriptions. By
representing each persona as a continuous vector, PersonaPKT learns implicit
persona-specific features directly from a small number of dialogue samples
produced by the same persona, adding less than 0.1% trainable parameters for
each persona on top of the PLM backbone. Empirical results demonstrate that
PersonaPKT effectively builds personalized DAs with high storage efficiency,
outperforming various baselines in terms of persona consistency while
maintaining good response generation quality. In addition, it enhances privacy
protection by avoiding explicit persona descriptions. Overall, PersonaPKT is an
effective solution for creating personalized DAs that respect user privacy.