Сравнение методов

	Многоязычная рекуррентная нейронная сеть	Мультиязычный трансформер
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	1990–2010s	2019–2020
Автор метода	Elman, J. L. (RNN); multilingual extension by NLP community	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Тип	Sequential model (cross-lingual)	Pre-trained cross-lingual language model
Основополагающий источник	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	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.
Другие названия	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Связанные	5	4
Сводка	A Multilingual Recurrent Neural Network (Multilingual RNN) applies the standard RNN architecture — which processes sequences step by step while maintaining a hidden state — to data spanning two or more languages. By training on multilingual corpora or sharing parameters across languages, the model learns cross-lingual sequence representations useful for translation, tagging, classification, and language modeling tasks.	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.