  Representation learning for text via pretraining a language model on a large
corpus has become a standard starting point for building NLP systems. This
approach stands in contrast to autoencoders, also trained on raw text, but with
the objective of learning to encode each input as a vector that allows full
reconstruction. Autoencoders are attractive because of their latent space
structure and generative properties. We therefore explore the construction of a
sentence-level autoencoder from a pretrained, frozen transformer language
model. We adapt the masked language modeling objective as a generative,
denoising one, while only training a sentence bottleneck and a single-layer
modified transformer decoder. We demonstrate that the sentence representations
discovered by our model achieve better quality than previous methods that
extract representations from pretrained transformers on text similarity tasks,
style transfer (an example of controlled generation), and single-sentence
classification tasks in the GLUE benchmark, while using fewer parameters than
large pretrained models.
