Papers › Iterative Edit-Based Unsupervised Sentence Simplification
Iterative Edit-Based Unsupervised Sentence Simplification
Dhruv Kumar, Lili Mou, Lukasz Golab, Olga Vechtomova
We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word and phrase-level edits on the complex sentence. Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable. Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Simplification | Newsela | Edit-Unsup-TS | BLEU | 17.36 | #5 of 13 | Archive leaderboard | report |
| Text Simplification | Newsela | Edit-Unsup-TS | SARI | 30.44 | #5 of 13 | Archive leaderboard | report |
| Text Simplification | TurkCorpus | Edit-Unsup-TS | BLEU | 73.62 | #11 of 25 | Archive leaderboard | report |
| Text Simplification | TurkCorpus | Edit-Unsup-TS | SARI (EASSE>=0.2.1) | 37.85 | #11 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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