Papers › Unsupervised Neural Text Simplification
Unsupervised Neural Text Simplification
Sai Surya, Abhijit Mishra, Anirban Laha, Parag Jain, Karthik Sankaranarayanan
The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora. The core framework is composed of a shared encoder and a pair of attentional-decoders and gains knowledge of simplification through discrimination based-losses and denoising. The framework is trained using unlabeled text collected from en-Wikipedia dump. Our analysis (both quantitative and qualitative involving human evaluators) on a public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods. Addition of a few labelled pairs also improves the performance further.
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Code
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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 | ASSET | UNTS (Unsupervised) | BLEU | 76.14* | #9 of 12 | Archive leaderboard | report |
| Text Simplification | ASSET | UNTS (Unsupervised) | SARI (EASSE>=0.2.1) | 35.19 | #9 of 12 | Archive leaderboard | report |
| Text Simplification | TurkCorpus | UNMT (Unsupervised) | BLEU | 74.02 | #15 of 25 | Archive leaderboard | report |
| Text Simplification | TurkCorpus | UNMT (Unsupervised) | SARI (EASSE>=0.2.1) | 37.20 | #15 of 25 | Archive leaderboard | report |
| Text Simplification | TurkCorpus | UNTS-10k (Weakly supervised) | SARI (EASSE>=0.2.1) | 37.15 | #16 of 25 | Archive leaderboard | report |
| Text Simplification | TurkCorpus | UNTS (Unsupervised) | SARI (EASSE>=0.2.1) | 36.29 | #20 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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