  Adopting contextually appropriate, audience-tailored linguistic styles is
critical to the success of user-centric language generation systems (e.g.,
chatbots, computer-aided writing, dialog systems). While existing approaches
demonstrate textual style transfer with large volumes of parallel or
non-parallel data, we argue that grounding style on audience-independent
external factors is innately limiting for two reasons. First, it is difficult
to collect large volumes of audience-specific stylistic data. Second, some
stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to
define without audience feedback.
  In this paper, we propose the novel task of style infusion - infusing the
stylistic preferences of audiences in pretrained language generation models.
Since humans are better at pairwise comparisons than direct scoring - i.e., is
Sample-A more persuasive/polite/empathic than Sample-B - we leverage limited
pairwise human judgments to bootstrap a style analysis model and augment our
seed set of judgments. We then infuse the learned textual style in a GPT-2
based text generator while balancing fluency and style adoption. With
quantitative and qualitative assessments, we show that our infusion approach
can generate compelling stylized examples with generic text prompts. The code
and data are accessible at https://github.com/CrowdDynamicsLab/StyleInfusion.
