Style transfer has been widely explored in natural language generation with
non-parallel corpus by directly or indirectly extracting a notion of style from
source and target domain corpus. A common shortcoming of existing approaches is
the prerequisite of joint annotations across all the stylistic dimensions under
consideration. Availability of such dataset across a combination of styles
limits the extension of these setups to multiple style dimensions. While
cascading single-dimensional models across multiple styles is a possibility, it
suffers from content loss, especially when the style dimensions are not
completely independent of each other. In our work, we relax this requirement of
jointly annotated data across multiple styles by using independently acquired
data across different style dimensions without any additional annotations. We
initialize an encoder-decoder setup with transformer-based language model
pre-trained on a generic corpus and enhance its re-writing capability to
multiple target style dimensions by employing multiple style-aware language
models as discriminators. Through quantitative and qualitative evaluation, we
show the ability of our model to control styles across multiple style
dimensions while preserving content of the input text. We compare it against
baselines involving cascaded state-of-the-art uni-dimensional style transfer
models.

本文介绍了使用独立获取的数据来控制多种风格的方法，旨在解决现有方法中需要联合注释所有风格维度的共同缺点，通过与现有技术的比较，我们的模型能够保留输入文本的内容，同时控制多个风格维度。