Salīdzināt metodes

	Daudzvalodu rekurentā neironu tīkls	Atkārtotais neironu tīkls
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1990–2010s	1986–1990
Autors	Elman, J. L. (RNN); multilingual extension by NLP community	Rumelhart, D. E.; Elman, J. L.
Tips	Sequential model (cross-lingual)	Sequential neural network
Pirmavots	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Citi nosaukumi	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN	RNN, Elman network, Jordan network, simple recurrent network
Saistītās	5	3
Kopsavilkums	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 Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.