论文标题
正式样式转移的并行数据增强
Parallel Data Augmentation for Formality Style Transfer
论文作者
论文摘要
正式风格转移任务中进步的主要障碍是训练数据不足。在本文中，我们研究了如何增强并行数据，并为此任务提出了新颖和简单的数据增强方法，以获取具有易于访问的模型和系统的有用句子对。实验表明，我们的增强并行数据在用于预先培训模型时很大程度上有助于改善形式样式的转移，从而导致最新的GYAFC基准数据集中最新。
The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.
