论文标题
稳定样式变压器：使用编码器删除和生成方法，用于文本样式传输
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
论文作者
论文摘要
文本样式转移是通过保留输入句子的内容并传输样式来生成句子的任务。大多数现有的研究都在非并行数据集上进行进展，因为并行数据集有限且难以构建。在这项工作中，我们介绍了一种遵循非并行数据集中两个阶段的方法。第一阶段是直接通过分类器删除句子的属性标记。第二阶段是通过组合内容令牌和目标样式来生成转移的句子。我们在两个基准数据集上进行实验，并评估上下文，样式，流利度和语义。很难仅使用这些自动指标选择最佳系统，但是可以选择稳定的系统。我们仅将所有自动评估指标中的强大系统视为可在实际应用中使用的最小条件。在某些情况下，许多先前的系统都难以使用，因为在几种评估指标中性能明显降低。但是，我们的系统在所有自动评估指标中都是稳定的，并且结果与其他模型相当。此外，我们通过人类评估比较系统的性能结果和不稳定的系统。我们的代码和数据可在链接（https://github.com/rungjoo/stable-style-transformer）上找到。
Text style transfer is the task that generates a sentence by preserving the content of the input sentence and transferring the style. Most existing studies are progressing on non-parallel datasets because parallel datasets are limited and hard to construct. In this work, we introduce a method that follows two stages in non-parallel datasets. The first stage is to delete attribute markers of a sentence directly through a classifier. The second stage is to generate a transferred sentence by combining the content tokens and the target style. We experiment on two benchmark datasets and evaluate context, style, fluency, and semantic. It is difficult to select the best system using only these automatic metrics, but it is possible to select stable systems. We consider only robust systems in all automatic evaluation metrics to be the minimum conditions that can be used in real applications. Many previous systems are difficult to use in certain situations because performance is significantly lower in several evaluation metrics. However, our system is stable in all automatic evaluation metrics and has results comparable to other models. Also, we compare the performance results of our system and the unstable system through human evaluation. Our code and data are available at the link (https://github.com/rungjoo/Stable-Style-Transformer).
