Сравнение на методи

	Многоезиков GRU	Многоезичен трансформер
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2014 (GRU); multilingual applications from ~2016	2019–2020
Създател	Cho, K. et al. (GRU); multilingual extension by NLP community	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Тип	Recurrent sequence model (multilingual)	Pre-trained cross-lingual language model
Основополагащ източник	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	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.
Други названия	Multilingual GRU, cross-lingual GRU, multilingual gated recurrent unit, multi-language GRU	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Свързани	4	4
Резюме	A Multilingual GRU is a Gated Recurrent Unit network trained on text data spanning multiple languages, enabling sequential modeling of language-sensitive tasks such as sentiment analysis, named entity recognition, and machine translation across language boundaries without requiring separate models per language.	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.