So sánh phương pháp

	Transformer đa ngôn ngữ	Nhúng câu đa ngôn ngữ
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019–2020	2019–2022
Người khởi xướng	Devlin et al. (mBERT); Conneau et al. (XLM-R)	Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)
Loại	Pre-trained cross-lingual language model	Cross-lingual representation learning
Công trình gốc	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.	Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.
Tên gọi khác	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
Liên quan	4	5
Tóm tắt	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.	Multilingual sentence embeddings map sentences from many languages into a single shared vector space so that semantically equivalent sentences — regardless of language — land close together. Models such as LaBSE, multilingual Sentence-BERT, and mUSE have made it practical to compare, retrieve, and classify text across 50 to 100+ languages without translating anything first.