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Text simplification (TS) can be viewed as monolingual translation task,translating between text variations within a single language. Recent neural TSmodels draw on insights from neural machine translation to learn lexicalsimplification and content reduction using encoder-decoder model. But differentfrom neural machine translation, we cannot obtain enough ordinary andsimplified sentence pairs for TS, which are expensive and time-consuming tobuild. Target-side simplified sentences plays an important role in boostingfluency for statistical TS, and we investigate the use of simplified sentencesto train, with no changes to the network architecture. We propose to pairsimple training sentence with a synthetic ordinary sentence viaback-translation, and treating this synthetic data as additional training data.We train encoder-decoder model using synthetic sentence pairs and originalsentence pairs, which can obtain substantial improvements on the availableWikiLarge data and WikiSmall data compared with the state-of-the-art methods.

Improving Neural Text Simplification Model with Simplified Corpora