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

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

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Text Simplification Newsela EditNTS SARI 31.41 # 3
BLEU 19.85 # 5
Text Simplification PWKP / WikiSmall EditNTS SARI 32.35 # 2
Text Simplification TurkCorpus EditNTS SARI (EASSE>=0.2.1) 38.22 # 8
BLEU 86.69 # 2


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