विधियों की तुलना करें

	बहुभाषी आवर्ती तंत्रिका नेटवर्क	गेटेड रिकरेंट यूनिट (GRU)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1990–2010s	2014
प्रवर्तक	Elman, J. L. (RNN); multilingual extension by NLP community	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
प्रकार	Sequential model (cross-lingual)	Recurrent neural network with gating
मौलिक स्रोत	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	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. In Proceedings of EMNLP 2014, pp. 1724–1734.
उपनाम	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN	GRU, GRU network, gated RNN, GRU cell
संबंधित	5	3
सारांश	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.	The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.