The paper presents a first attempt towards unsupervised neural text
simplification that relies only on unlabeled text corpora. The core framework
is composed of a shared encoder and a pair of attentional-decoders and gains
knowledge of simplification through discrimination based-losses and denoising.
The framework is trained using unlabeled text collected from en-Wikipedia dump.
Our analysis (both quantitative and qualitative involving human evaluators) on
a public test data shows that the proposed model can perform
text-simplification at both lexical and syntactic levels, competitive to
existing supervised methods. Addition of a few labelled pairs also improves the
performance further.

本文提出了第一次尝试仅依赖于未标记文本语料库进行无监督神经文本简化的核心框架，由共享编码器和一对注意解码器组成，并通过基于鉴别和去噪的损失进行简化知识的获取，并使用从 en-Wikipedia 转储的未标记文本进行训练。我们在公共测试数据上进行的分析（包括定量和定性的人类评估）表明，所提出的模型可以在词汇和句法两个级别上进行文本简化，竞争现有的监督方法。此外，增加几个标记的对也进一步提高了性能。