Abstract
Text style transfer rephrases a text from a source style (e.g., informal) to a target style (e.g., formal) while keeping its original meaning. Despite the success existing works have achieved using a parallel corpus for the two styles, transferring text style has proven significantly more challenging when there is no parallel training corpus. In this paper, we address this challenge by using a reinforcement-learning-based generator-evaluator architecture. Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style. Our evaluator is an adversarially trained style discriminator with semantic and syntactic constraints that score the generated sentence for style, meaning preservation, and fluency. Experimental results on two different style transfer tasks (sentiment transfer and formality transfer) show that our model outperforms state-of-the-art approaches. Furthermore, we perform a manual evaluation that demonstrates the effectiveness of the proposed method using subjective metrics of generated text quality.
Abstract (translated)
文本样式转换将文本从源样式（例如，非正式）改为目标样式（例如，正式），同时保留其原始含义。尽管现有的工作已经成功地使用了两种样式的并行语料库，但是在没有并行训练语料库的情况下，传输文本样式已经被证明具有更大的挑战性。在本文中，我们使用一个基于强化学习的生成器评估器体系结构来解决这个挑战。我们的生成器使用基于注意的编码器解码器将句子从源样式传输到目标样式。我们的评价者是一个受到对手训练的风格鉴别器，具有语义和句法约束，能够对生成的句子进行风格、语义保留和流畅性评分。对两种不同类型转移任务（情感转移和形式转移）的实验结果表明，我们的模型优于最先进的方法。此外，我们还进行了一次手动评估，证明了使用生成文本质量的主观指标的方法的有效性。
URL
https://arxiv.org/abs/1903.10671
