  Conventional text style transfer approaches focus on sentence-level style
transfer without considering contextual information, and the style is described
with attributes (e.g., formality). When applying style transfer in
conversations such as task-oriented dialogues, existing approaches suffer from
these limitations as context can play an important role and the style
attributes are often difficult to define in conversations. In this paper, we
introduce conversation style transfer as a few-shot learning problem, where the
model learns to perform style transfer by observing only a few example
dialogues in the target style. We propose a novel in-context learning approach
to solve the task with style-free dialogues as a pivot. Human evaluation shows
that by incorporating multi-turn context, the model is able to match the target
style while having better appropriateness and semantic correctness compared to
utterance/sentence-level style transfer. Additionally, we show that
conversation style transfer can also benefit downstream tasks. For example, in
multi-domain intent classification tasks, the F1 scores improve after
transferring the style of training data to match the style of the test data.
