---
title: "MUSS: Multilingual Unsupervised Sentence — Martin 2022"
description: "Sentence simplification has relied on aligned complex–simple corpora, which are small, mostly English, and tied to particular simplification guidelines."
authors: ["Louis Martin", "Angela Fan", "Éric de la Clergerie", "Antoine Bordes", "Benoît Sagot"]
year: 2022
venue: "Proceedings of the Thirteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2022.lrec-1.176/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/martin-2022-muss-multilingual-unsupervised-sentence-simplification/"
---
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# MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases

**Authors:** [Louis Martin](https://md.research.elamur.ai/free/authors/louis-martin/), [Angela Fan](https://md.research.elamur.ai/free/authors/angela-fan/), Éric de la Clergerie, [Antoine Bordes](https://md.research.elamur.ai/free/authors/antoine-bordes/), [Benoît Sagot](https://md.research.elamur.ai/free/authors/benoit-sagot/) · **Year:** 2022 · **Venue:** Proceedings of the Thirteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.lrec-1.176/) · [PDF](https://aclanthology.org/2022.lrec-1.176.pdf) · [OpenAlex](https://openalex.org/W3153637831)

**Citations:** 31 citations (openalex-snapshot · 2026-10-14) · [86](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Sentence simplification has relied on aligned complex–simple corpora, which are small, mostly English, and tied to particular simplification guidelines. MUSS avoids this by training simplification models on sentence-level paraphrases instead of human-made simplifications. The authors mine these paraphrases from CCNet, a cleaned extraction of Common Crawl. They embed short multi-sentence chunks with LASER, search for near neighbours with faiss, and filter out weak or trivial matches. A BART- or mBART-based seq2seq model is then trained with ACCESS control tokens. These tokens let the user steer length, lexical complexity, syntactic depth and amount of rewriting at inference time, so the output is a simplification rather than a plain paraphrase. Without any labeled simplification data, MUSS equals or surpasses the best earlier supervised systems for English, French and Spanish. Adding labeled data improves English results further. This matters because the same recipe can, in principle, produce simplification systems for languages that have no parallel simplification corpus. The authors released the models, the mined data and the code.

## Key points
- **Mined corpora:** about 1.19M pairs in English, 1.36M in French and 1.0M in Spanish. For comparison, WikiLarge has ~296K pairs and Newsela ~94K. The pairs were mined from 1 billion candidate sequences each for English and French, and 650M for Spanish.
- **Mining pipeline:**
  - Sequences are up to 300 characters and may contain several sentences.
  - Noisy sequences are removed using a punctuation-ratio cutoff (>10%) and a kenlm 3-gram Wikipedia language model.
  - For each sequence, LASER + faiss retrieve the top-8 neighbours by L2 distance.
  - Neighbours are kept only if they pass a distance bound and a margin criterion.
  - Near-identical pairs (Levenshtein ≤ 20%), nested pairs and overlapping-window pairs are discarded.
- **ACCESS control tokens:** `<NumChars>`, `<LevSim>` (Levenshtein similarity restricted to replace operations), `<WordFreq>` and `<DepTreeDepth>`. Each is a target-to-source ratio and is prepended to the input.
- **Unsupervised results:** MUSS (BART) trained only on mined data reaches SARI 42.65 on ASSET, 40.85 on TurkCorpus and 38.09 on Newsela (test sets). Supervised MUSS on WikiLarge + MINED reaches 44.15 SARI on ASSET.
- **Comparison with ParaNMT:** mined data performs on par with the existing ParaNMT paraphrase corpus (ASSET 42.65 vs 42.50, TurkCorpus 40.85 vs 40.50, Newsela 38.09 vs 39.11).

## Interesting ideas
- **Paraphrases plus controls instead of simplifications.** The authors first tried to mine simplifications directly. Mining generic paraphrases and imposing simplicity through control tokens worked better and required fewer assumptions. This suggests that a broad, easy-to-obtain parallel signal combined with controllable decoding can stand in for scarce task-specific data in other rewriting tasks.
- **Mining multi-sentence chunks.** Using spans of adjacent sentences, rather than single sentences, as mining units lets the data contain sentence splitting and fusion. Any monolingual alignment effort aimed at rewriting could adopt this.
- **Removing trivial pairs.** Discarding near-copies, contained sequences and pairs from overlapping windows forces the model to actually rewrite. This is a cheap, reusable cleaning step for mined parallel data.
- **Oracle controls at training time, chosen targets at inference.** Each training example receives its true attribute ratios as tokens, so a single model can later serve different audiences or benchmarks just by changing the requested values.
- **Setting controls with almost no aligned data.** Picking control values from only 50 unaligned validation sentences costs little SARI compared with full tuning on aligned data (e.g., 42.49 vs 42.65 on ASSET). This is valuable when evaluation data in a new language is tiny.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/shelves/machine-translation/)

## Cited by

Cited by

86 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 79 not yet.

- [OasisSimp: An Open-source Asian-English Sentence Simplification Dataset](https://md.research.elamur.ai/free/papers/liu-2026-oasissimp-open-source-asian-english/)
- [LENS: A Learnable Evaluation Metric for Text Simplification](https://md.research.elamur.ai/free/papers/maddela-2023-lens-learnable-evaluation-metric-text/)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/papers/min-2021-recent-advances-natural-language-processing/)
- [Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources](https://md.research.elamur.ai/free/papers/papucci-2026-controllable-sentence-simplification-italian-fine/)
- [Proficiency-Controlled Text Simplification in European Portuguese: A Preliminary Study using Prompting Approaches](https://md.research.elamur.ai/free/papers/ribeiro-2026-proficiency-controlled-text-simplification-european/)
- [Exploring Cognitively Informed Sentence Simplification with Gaze-Guided Text Generation](https://md.research.elamur.ai/free/papers/sauberli-2026-exploring-cognitively-informed-sentence-simplification/)
- [LegalSim-PT: Building a Dataset for Legal Document Simplification in Portuguese Leveraging Linguistic Metrics](https://md.research.elamur.ai/free/papers/scalercio-2026-legalsim-pt-building-dataset-legal/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{martin2022muss,
  title = {{MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases}},
  author = {Martin, Louis and Fan, Angela and Clergerie, Éric de la and Bordes, Antoine and Sagot, Benoît},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  year = {2022},
  doi = {10.63317/3kbbve8f2ey4},
  url = {https://doi.org/10.63317/3kbbve8f2ey4}
}
```

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- [next → NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://md.research.elamur.ai/free/papers/muhammad-2022-naijasenti-nigerian-twitter-sentiment-corpus/)
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