论文标题
通过对比度学习的梯度指导的无监督文本样式转移
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning
论文作者
论文摘要
文本样式转移是一个具有挑战性的文本生成问题，旨在将给定句子的样式更改为目标，同时保持其内容不变。由于平行数据集存在自然稀缺性，因此最近的作品主要集中于以无监督的方式解决该问题。但是，以前基于梯度的作品通常遭受如下的缺陷，即：（1）内容迁移。以前的方法缺乏内容不变性的明确建模，因此容易在原始句子和转移的句子之间发生变化。 （2）样式错误分类。梯度引导方法的自然缺点是，推理过程与一系列对抗性攻击是均匀的，因此由于错误分类，使潜在的优化很容易成为对分类器的攻击。这导致难以实现高传递精度。为了解决这些问题，我们通过文本样式转移的对比范式提出了一个新颖的梯度引导模型，明确地收集了类似的语义句子，并设计了基于暹罗结构的样式分类器，以分别减轻这两个问题。与最先进的图案相比，两个数据集上的实验显示了我们提出的方法的有效性。
Text style transfer is a challenging text generation problem, which aims at altering the style of a given sentence to a target one while keeping its content unchanged. Since there is a natural scarcity of parallel datasets, recent works mainly focus on solving the problem in an unsupervised manner. However, previous gradient-based works generally suffer from the deficiencies as follows, namely: (1) Content migration. Previous approaches lack explicit modeling of content invariance and are thus susceptible to content shift between the original sentence and the transferred one. (2) Style misclassification. A natural drawback of the gradient-guided approaches is that the inference process is homogeneous with a line of adversarial attack, making latent optimization easily becomes an attack to the classifier due to misclassification. This leads to difficulties in achieving high transfer accuracy. To address the problems, we propose a novel gradient-guided model through a contrastive paradigm for text style transfer, to explicitly gather similar semantic sentences, and to design a siamese-structure based style classifier for alleviating such two issues, respectively. Experiments on two datasets show the effectiveness of our proposed approach, as compared to the state-of-the-arts.
