  Adapting a large language model for multiple-attribute text style transfer
via fine-tuning can be challenging due to the significant amount of
computational resources and labeled data required for the specific task. In
this paper, we address this challenge by introducing AdapterTST, a framework
that freezes the pre-trained model's original parameters and enables the
development of a multiple-attribute text style transfer model. Using BART as
the backbone model, Adapter-TST utilizes different neural adapters to capture
different attribute information, like a plug-in connected to BART. Our method
allows control over multiple attributes, like sentiment, tense, voice, etc.,
and configures the adapters' architecture to generate multiple outputs
respected to attributes or compositional editing on the same sentence. We
evaluate the proposed model on both traditional sentiment transfer and
multiple-attribute transfer tasks. The experiment results demonstrate that
Adapter-TST outperforms all the state-of-the-art baselines with significantly
lesser computational resources. We have also empirically shown that each
adapter is able to capture specific stylistic attributes effectively and can be
configured to perform compositional editing.
