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      "statement": "# XLM and XLM-R: cross-lingual pretraining and multilingual scale analysis\n\nStudied shared multilingual causal/masked language objectives and parallel-sentence translation language modeling; XLM-R extended masked pretraining with larger filtered web corpora, vocabulary and capacity tradeoff analysis.\n\n## Scientific scope\n\nXLM's parallel-data objective is supervised; XLM-R's monolingual pretraining does not imply all evaluations are unsupervised. Improvements inherit BERT/RoBERTa and depend on sampling, capacity and resource distribution. Original v1 results differ from later revised abstracts, so no cross-version numerical claim is imported.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nGuillaume Lample; Alexis Conneau; Kartikay Khandelwal; Naman Goyal; Vishrav Chaudhary; Guillaume Wenzek; Francisco Guzmán; Edouard Grave; Myle Ott; Luke Zettlemoyer; Veselin Stoyanov\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291v1)\n- [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116v1)\n",
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