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。

多种样式训练数据分布对多样式文本转换的平衡效应

Balancing Effect of Training Dataset Distribution of Multiple Styles for  Multi-Style Text Transfer

Problems at the intersection of vision and language are of significant
importance both as challenging research questions and for the rich set of
applications they enable. However, inherent structure in our world and bias in
our language tend to be a simpler signal for learning than visual modalities,
resulting in models that ignore visual information, leading to an inflated
sense of their capability.
We propose to counter these language priors for the task of Visual Question
Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance
the popular VQA dataset by collecting complementary images such that every
question in our balanced dataset is associated with not just a single image,
but rather a pair of similar images that result in two different answers to the
question. Our dataset is by construction more balanced than the original VQA
dataset and has approximately twice the number of image-question pairs. Our
complete balanced dataset is publicly available at www.visualqa.org as part of
the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA
v2.0).
We further benchmark a number of state-of-art VQA models on our balanced
dataset. All models perform significantly worse on our balanced dataset,
suggesting that these models have indeed learned to exploit language priors.
This finding provides the first concrete empirical evidence for what seems to
be a qualitative sense among practitioners.
Finally, our data collection protocol for identifying complementary images
enables us to develop a novel interpretable model, which in addition to
providing an answer to the given (image, question) pair, also provides a
counter-example based explanation. Specifically, it identifies an image that is
similar to the original image, but it believes has a different answer to the
same question. This can help in building trust for machines among their users.

该研究通过收集相关的图像，用于构建一个平衡的数据集（VQA v2.0），并在此基础上比较现有的 VQA 模型的表现，发现这些模型都受到了语言先验的影响。此外，该研究还提出了一种可解释性模型，它不仅可以回答问题，而且还可以通过提供一个相似但不同的图像来提高用户对其信任度。