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.

本文提出了一种基于无监督机器翻译的文本简化方法，使用 back-translation 架构和噪声自编码器生成平行语料，引入不同类型的噪声进行训练的不对称去噪方法可以显著提高简化性能。模型在无监督和半监督学习中均具备较好性能，能够与当前多种最先进的简化系统竞争。