EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
Yue Dong, Zichao Li, Mehdi Rezagholizadeh, Jackie Chi Kit Cheung · 2019
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 1 .