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Formality style transformation is the task of modifying the formality of agiven sentence without changing its content. Its challenge is the lack oflarge-scale sentence-aligned parallel data. In this paper, we propose anomnivorous model that takes parallel data and formality-classified data jointlyto alleviate the data sparsity issue. We empirically demonstrate theeffectiveness of our approach by achieving the state-of-art performance on arecently proposed benchmark dataset of formality transfer. Furthermore, ourmodel can be readily adapted to other unsupervised text style transfer taskslike unsupervised sentiment transfer and achieve competitive results on threewidely recognized benchmarks.

Formality Style Transfer with Hybrid Textual Annotations