So sánh phương pháp

	Bộ tự mã hóa biến phân đa ngôn ngữ	Transformer đa ngôn ngữ
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2017-2018	2019–2020
Người khởi xướng	Multiple research groups (Lample, Conneau et al.; Zhao et al.)	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Loại	Generative latent-variable model	Pre-trained cross-lingual language model
Công trình gốc	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.
Tên gọi khác	ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Liên quan	5	4
Tóm tắt	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.