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We present the first sentence simplification model that learns explicit editoperations (ADD, DELETE, and KEEP) via a neural programmer-interpreterapproach. Most current neural sentence simplification systems are variants ofsequence-to-sequence models adopted from machine translation. These methodslearn to simplify sentences as a byproduct of the fact that they are trained oncomplex-simple sentence pairs. By contrast, our neural programmer-interpreteris directly trained to predict explicit edit operations on targeted parts ofthe input sentence, resembling the way that humans might perform simplificationand revision. Our model outperforms previous state-of-the-art neural sentencesimplification models (without external knowledge) by large margins on threebenchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89WikiSmall, +1.41 Newsela), and is judged by humans to produce overall betterand simpler output sentences.

EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing