With advances in neural language models, the focus of linguistic
steganography has shifted from edit-based approaches to generation-based ones.
While the latter's payload capacity is impressive, generating genuine-looking
texts remains challenging. In this paper, we revisit edit-based linguistic
steganography, with the idea that a masked language model offers an
off-the-shelf solution. The proposed method eliminates painstaking rule
construction and has a high payload capacity for an edit-based model. It is
also shown to be more secure against automatic detection than a
generation-based method while offering better control of the security/payload
capacity trade-off.

本文提出了一种基于掩码语言模型的语言隐写术，它克服了传统编辑方法的繁琐规则构建，并具有更高的负载能力和更好的安全性能。此方法比生成方法更易于控制安全性和负载能力之间的权衡。

基于掩码语言模型的令人沮丧的基于编辑的语言隐写术

Frustratingly Easy Edit-based Linguistic Steganography with a Masked  Language Model

Edit-based approaches have recently shown promising results on multiple
monolingual sequence transduction tasks. In contrast to conventional
sequence-to-sequence (Seq2Seq) models, which learn to generate text from
scratch as they are trained on parallel corpora, these methods have proven to
be much more effective since they are able to learn to make fast and accurate
transformations while leveraging powerful pre-trained language models. Inspired
by these ideas, we present TST, a simple and efficient Text Simplification
system based on sequence Tagging, leveraging pre-trained Transformer-based
encoders. Our system makes simplistic data augmentations and tweaks in training
and inference on a pre-existing system, which makes it less reliant on large
amounts of parallel training data, provides more control over the outputs and
enables faster inference speeds. Our best model achieves near state-of-the-art
performance on benchmark test datasets for the task. Since it is fully
non-autoregressive, it achieves faster inference speeds by over 11 times than
the current state-of-the-art text simplification system.

本论文介绍了一种基于序列标记的简单，高效的文本简化系统 (TST), 使用预训练的 Transformer-based 编码器，在现有系统的训练和推断中进行简单的数据增强和调整，使其不太依赖于大量的平行训练数据，提供了更多对输出的控制并实现了更快的推断速度，目前取得了基准测试数据集任务的几乎最新性能。