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

Yue Dong, Zichao Li, Mehdi Rezagholizadeh, Jackie Chi Kit Cheung


Abstract
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 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.
Anthology ID:
P19-1331
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3393–3402
URL:
https://www.aclweb.org/anthology/P19-1331
DOI:
10.18653/v1/P19-1331
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PDF:
https://www.aclweb.org/anthology/P19-1331.pdf