Before revision:  Previous work has aligned sentences from original and simplified corpora such as English Wikipedia and Simple English Wikipedia, but this limits corpus size, domain, and language. In this work, we propose using unsupervised mining techniques to automatically create training corpora for simplification in multiple languages from raw Common Crawl web data . When coupled with a controllable generation mechanism that can flexibly adjust attributes such as length and lexical complexity , these mined paraphrase corpora can be used to train simplification systems in any language .
After revision:  We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. These models leverage unsupervised pretraining and controllable generation mechanisms to flexibly adjust attributes such as length and lexical complexity , these mined paraphrase corpora can be used to train simplification systems in any language .