विधियों की तुलना करें

	बहुभाषी आवर्ती तंत्रिका नेटवर्क	लॉन्ग शॉर्ट-टर्म मेमोरी (LSTM)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1990–2010s	1997
प्रवर्तक	Elman, J. L. (RNN); multilingual extension by NLP community	Hochreiter, S. & Schmidhuber, J.
प्रकार	Sequential model (cross-lingual)	Recurrent neural network with gated memory cells
मौलिक स्रोत	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
उपनाम	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
संबंधित	5	4
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.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.