  In this paper we present a simple re-ranking method for Automatic Sentence
Simplification based on the noisy channel scheme. Instead of directly computing
the best simplification given a complex text, the re-ranking method also
considers the probability of the simple sentence to produce the complex
counterpart, as well as the probability of the simple text itself, according to
a language model. Our experiments show that combining these scores outperform
the original system in three different English datasets, yielding the best
known result in one of them. Adopting the noisy channel scheme opens new ways
to infuse additional information into ATS systems, and thus to control
important aspects of them, a known limitation of end-to-end neural seq2seq
generative models.
