Text style transfer (TST) aims to vary the style polarity of text while
preserving the semantic content. Although recent advancements have demonstrated
remarkable progress in short TST, it remains a relatively straightforward task
with limited practical applications. The more comprehensive long TST task
presents two challenges: (1) existing methods encounter difficulties in
accurately evaluating content attributes in multiple words, leading to content
degradation; (2) the conventional vanilla style classifier loss encounters
obstacles in maintaining consistent style across multiple generated sentences.
In this paper, we propose a novel method SC2, where a multilayer Joint
Style-Content Weighed (JSCW) module and a Style Consistency loss are designed
to address the two issues. The JSCW simultaneously assesses the amounts of
style and content attributes within a token, aiming to acquire a lossless
content representation and thereby enhancing content preservation. The multiple
JSCW layers further progressively refine content representations. We design a
style consistency loss to ensure the generated multiple sentences consistently
reflect the target style polarity. Moreover, we incorporate a denoising
non-autoregressive decoder to accelerate the training. We conduct plentiful
experiments and the results show significant improvements of SC2 over
competitive baselines. Our code: this https URL

我们提出了一种新颖的方法 SC2，其中设计了多层联合风格 - 内容加权（JSCW）模块和风格一致性损失，以解决现有方法在准确评估多词内容属性和保持一致风格方面的困难，进而改善内容保留和风格一致性。

SC2：在长文本风格转移中增强内容保持和风格一致性

SC2: Towards Enhancing Content Preservation and Style Consistency in  Long Text Style Transfer

In real teaching scenarios, an excellent teacher always teaches what he (or
she) is good at but the student is not. This gives the student the best
assistance in making up for his (or her) weaknesses and becoming a good one
overall. Enlightened by this, we introduce the "Teaching what you Should Teach"
strategy into a knowledge distillation framework, and propose a data-based
distillation method named "TST" that searches for desirable augmented samples
to assist in distilling more efficiently and rationally. To be specific, we
design a neural network-based data augmentation module with priori bias, which
assists in finding what meets the teacher's strengths but the student's
weaknesses, by learning magnitudes and probabilities to generate suitable data
samples. By training the data augmentation module and the generalized
distillation paradigm in turn, a student model is learned with excellent
generalization ability. To verify the effectiveness of our method, we conducted
extensive comparative experiments on object recognition, detection, and
segmentation tasks. The results on the CIFAR-10, ImageNet-1k, MS-COCO, and
Cityscapes datasets demonstrate that our method achieves state-of-the-art
performance on almost all teacher-student pairs. Furthermore, we conduct
visualization studies to explore what magnitudes and probabilities are needed
for the distillation process.

该研究提出了一种名为 “Teaching what you Should Teach” 的策略，将其引入到知识蒸馏框架中。通过神经网络数据增强模块，该数据驱动蒸馏方法可以在查找适当的数据样本时帮助学生弥补其弱点，让学生成为全面优秀的人。此方法在目标识别、检测和分割任务上，通过详细的比较实验和探索性研究，证明它具有先进的性能和一定的推广能力。