  Text simplification (TS) can be viewed as monolingual translation task,
translating between text variations within a single language. Recent neural TS
models draw on insights from neural machine translation to learn lexical
simplification and content reduction using encoder-decoder model. But different
from neural machine translation, we cannot obtain enough ordinary and
simplified sentence pairs for TS, which are expensive and time-consuming to
build. Target-side simplified sentences plays an important role in boosting
fluency for statistical TS, and we investigate the use of simplified sentences
to train, with no changes to the network architecture. We propose to pair
simple training sentence with a synthetic ordinary sentence via
back-translation, and treating this synthetic data as additional training data.
We train encoder-decoder model using synthetic sentence pairs and original
sentence pairs, which can obtain substantial improvements on the available
WikiLarge data and WikiSmall data compared with the state-of-the-art methods.
