  Text simplification (TS) rephrases long sentences into simplified variants
while preserving inherent semantics. Traditional sequence-to-sequence models
heavily rely on the quantity and quality of parallel sentences, which limits
their applicability in different languages and domains. This work investigates
how to leverage large amounts of unpaired corpora in TS task. We adopt the
back-translation architecture in unsupervised machine translation (NMT),
including denoising autoencoders for language modeling and automatic generation
of parallel data by iterative back-translation. However, it is non-trivial to
generate appropriate complex-simple pair if we directly treat the set of simple
and complex corpora as two different languages, since the two types of
sentences are quite similar and it is hard for the model to capture the
characteristics in different types of sentences. To tackle this problem, we
propose asymmetric denoising methods for sentences with separate complexity.
When modeling simple and complex sentences with autoencoders, we introduce
different types of noise into the training process. Such a method can
significantly improve the simplification performance. Our model can be trained
in both unsupervised and semi-supervised manner. Automatic and human
evaluations show that our unsupervised model outperforms the previous systems,
and with limited supervision, our model can perform competitively with multiple
state-of-the-art simplification systems.
