Sentence Bottleneck Autoencoders from Transformer Language Models
This work addresses the need for efficient and high-quality sentence representations in NLP, offering an incremental improvement over existing extraction methods.
The paper tackled the problem of learning sentence-level representations by constructing an autoencoder from a frozen pretrained transformer language model, achieving better quality than previous methods on text similarity, style transfer, and GLUE classification tasks with fewer parameters.
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.