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.

本论文介绍了 AdapterTST，它利用神经适配器冻结预训练模型的原始参数，可以控制不同的属性，包括情感、时态、语音等，并配置适配器的架构以生成多个输出，同时还可以进行组成式编辑。实验结果表明，Adapter-TST 模型能够更好地捕捉特定风格属性，并具有更少的计算资源消耗。