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.

通过平衡训练数据集中每种风格的分布，我们发现均衡的数据集比不平衡或偏斜的数据集产生更有效的多样式控制效果，并通过定量分析探索了训练数据中多种样式分布对风格转换输出的影响，这些结果将更好地指导风格转移数据集的设计。