পদ্ধতির তুলনা করুন

	বহুভাষিক পুনরাবৃত্ত নিউরাল নেটওয়ার্ক	গেটেড রিকারেন্ট ইউনিট (GRU)
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1990–2010s	2014
প্রবর্তক	Elman, J. L. (RNN); multilingual extension by NLP community	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.
ধরন	Sequential model (cross-lingual)	Recurrent neural network with gating
মৌলিক উৎস	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.
অপর নাম	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN	GRU, GRU network, gated RNN, GRU cell
সম্পর্কিত	5	3
সারসংক্ষেপ	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.