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	Wielojęzyczny autoenkoder wariacyjny	Wielojęzyczny transformator
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2017-2018	2019–2020
Twórca	Multiple research groups (Lample, Conneau et al.; Zhao et al.)	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Typ	Generative latent-variable model	Pre-trained cross-lingual language model
Źródło pierwotne	Zhao, T., Zhang, Y., & Eskenazi, M. (2018). Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (pp. 1-10). ACL.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
Inne nazwy	ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Pokrewne	5	4
Podsumowanie	A Multilingual Variational Autoencoder (ML-VAE) extends the standard VAE framework to handle multiple languages within a shared probabilistic latent space. Language-specific encoders map text from each language into a common continuous representation, while language-specific decoders reconstruct or translate that text. This enables cross-lingual generation, style transfer, and representation learning with or without parallel corpora.	A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.