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Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer

机译：无监督文本转移的循环一致的对手自动化器

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摘要

Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach to unsupervised text style transfer which we refer to as Cycle-consistent Adversarial autoEncoders (CAE) trained from non-parallel data. CAE consists of three essential components: (1) LSTM autoencoders that encode a text in one style into its latent representation and decode an encoded representation into its original text or a transferred representation into a style-transferred text, (2) adversarial style transfer networks that use an adversarially trained generator to transform a latent representation in one style into a representation in another style, and (3) a cycle-consistent constraint that enhances the capacity of the adversarial style transfer networks in content preservation. The entire CAE with these three components can be trained end-to-end. Extensive experiments and in-depth analyses on two widely-used public datasets consistently validate the effectiveness of proposed CAE in both style transfer and content preservation against several strong baselines in terms of four automatic evaluation metrics and human evaluation.
机译：由于缺乏并行数据和内容保存困难，无监督的文本方式传输充满挑战。在本文中，我们提出了一种新的神经方法，以便无监督的文本方式转移，我们将从非并行数据培训的循环一致的对抗性自动化器（CAE）称为循环一致的对抗性AutoEnders（CAE）。 CAE由三个基本组件组成：（1）将一个样式中的文本编码为其潜在表示的LSTM AutoEncoders，并将编码的表示解码为其原始文本或转移的表示转换为样式传输的文本，（2）对冲样式转移网络这使用过的训练有素的生成器将一种风格转换为另一个样式的潜在表示，并且（3）循环一致的约束，其增强了内容保存中的对抗式样式转移网络的容量。整个CAE具有这三个部件的端到端可以训练。在两个广泛使用的公共数据集中进行了广泛的实验和深入分析，始终如一地验证所提出的CAE在四种自动评估指标和人类评估方面对若干强基线的风格转移和内容保存的有效性。

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