  As the functionality of dialogue systems evolves, hybrid dialogue systems
that accomplish user-specific goals and participate in open-topic chitchat with
users are attracting growing attention. Existing research learns both tasks
concurrently utilizing a multi-task fusion technique but ignores the negative
transfer phenomenon induced by the unique textual style differences. Therefore,
contrastive learning based on the latent variable model is used to decouple the
various textual genres in the latent space. We devise supervised and
self-supervised positive and negative sample constructions for diverse
datasets. In addition, to capitalize on the style information contained in the
decoupled latent variables, we employ a style prefix that incorporates latent
variables further to control the generation of responses with varying styles.
We performed extensive experiments on three dialogue datasets, including a
hybrid dialogue dataset and two task-oriented dialogue datasets. The
experimental results demonstrate that our method can mitigate the negative
style transfer issue and achieves state-of-the-art performance on multiple
dialogue datasets.
