Binary classifiers are often employed as discriminators in GAN-based
unsupervised style transfer systems to ensure that transferred sentences are
similar to sentences in the target domain. One difficulty with this approach is
that the error signal provided by the discriminator can be unstable and is
sometimes insufficient to train the generator to produce fluent language. In
this paper, we propose a new technique that uses a target domain language model
as the discriminator, providing richer and more stable token-level feedback
during the learning process. We train the generator to minimize the negative
log likelihood (NLL) of generated sentences, evaluated by the language model.
By using a continuous approximation of discrete sampling under the generator,
our model can be trained using back-propagation in an end- to-end fashion.
Moreover, our empirical results show that when using a language model as a
structured discriminator, it is possible to forgo adversarial steps during
training, making the process more stable. We compare our model with previous
work using convolutional neural networks (CNNs) as discriminators and show that
our approach leads to improved performance on three tasks: word substitution
decipherment, sentiment modification, and related language translation.

本研究提出了一种新的方法，使用目标域语言模型作为辨别器，为生成模型提供更丰富和更稳定的令牌级反馈，以便用于二进制分类器在 GAN-based 无监督风格转换系统中。该模型可使用从生成模型下的连续近似离散采样而训练，且相较于以前使用卷积神经网络（CNN）作为辨别器的先前工作，我们的方法在单词替换解密、情感修改和相关语言翻译三项任务上表现出了更好的性能，同时可以省略训练期间的对抗步骤，使过程更加稳定。