  Text style transfer is an exciting task within the field of natural language
generation that is often plagued by the need for high-quality paired datasets.
Furthermore, training a model for multi-attribute text style transfer requires
datasets with sufficient support across all combinations of the considered
stylistic attributes, adding to the challenges of training a style transfer
model. This paper explores the impact of training data input diversity on the
quality of the generated text from the multi-style transfer model. We construct
a pseudo-parallel dataset by devising heuristics to adjust the style
distribution in the training samples. We balance our training dataset using
marginal and joint distributions to train our style transfer models. We observe
that a balanced dataset produces more effective control effects over multiple
styles than an imbalanced or skewed one. Through quantitative analysis, we
explore the impact of multiple style distributions in training data on
style-transferred output. These findings will better inform the design of
style-transfer datasets.
