  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.
