  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.
