Formality style transformation is the task of modifying the formality of a given sentence without changing its content. Its challenge is the lack of large-scale sentence-aligned parallel data. In this paper, we propose an omnivorous model that takes parallel data and formality-classified data jointly to alleviate the data sparsity issue. We empirically demonstrate the effectiveness of our approach by achieving the state-of-art performance on a recently proposed benchmark dataset of formality transfer. Furthermore, our model can be readily adapted to other unsupervised text style transfer tasks like unsupervised sentiment transfer and achieve competitive results on three widely recognized benchmarks.

翻译：形式风格的转变是修改某一句的正规性而不改变其内容的任务,挑战在于缺乏与判决一致的大规模平行数据。在本文中,我们建议采用一个综合模式,将平行数据和形式分类数据联合起来,以缓解数据广度问题。我们从经验上证明了我们的方法的有效性,在最近提议的关于形式转移的基准数据集上取得了最先进的业绩。此外,我们的模式可以很容易地适应其他不受监督的文本风格传输任务,如未经监督的情绪传输,并在三大公认基准上取得竞争性成果。