The performance of existing text style transfer models is severely limited by
the non-parallel datasets on which the models are trained. In non-parallel
datasets, no direct mapping exists between sentences of the source and target
style; the style transfer models thus only receive weak supervision of the
target sentences during training, which often leads the model to discard too
much style-independent information, or utterly fail to transfer the style. In
this work, we propose LaMer, a novel text style transfer framework based on
large-scale language models. LaMer first mines the roughly parallel expressions
in the non-parallel datasets with scene graphs, and then employs MLE training,
followed by imitation learning refinement, to leverage the intrinsic
parallelism within the data. On two benchmark tasks (sentiment & formality
transfer) and a newly proposed challenging task (political stance transfer),
our model achieves qualitative advances in transfer accuracy, content
preservation, and fluency. Further empirical and human evaluations demonstrate
that our model not only makes training more efficient, but also generates more
readable and diverse expressions than previous models.

LaMer 是一种基于大型语言模型的新型文本风格转换框架，通过场景图挖掘非平行数据中的近似平行表达，以利用数据内在的平行性。在情感、正式性和政治立场转换任务中，我们的模型在转换准确性、内容保留和流畅性方面实现了质的飞跃。进一步的实证和人类评估表明，我们的模型不仅使训练更有效，而且比以前的模型生成更易读和更多样化的表达式。