Comparer des méthodes

	LSTM multilingue	Long Short-Term Memory (LSTM)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	1997 (LSTM); multilingual NLP applications from ~2016	1997
Auteur d'origine	Hochreiter, S. & Schmidhuber, J. (LSTM base); multilingual application by the NLP community from ~2016	Hochreiter, S. & Schmidhuber, J.
Type	Recurrent neural network (sequence model)	Recurrent neural network with gated memory cells
Source fondatrice	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Alias	Multilingual LSTM, Cross-lingual LSTM, Multi-language LSTM, Multilingual Recurrent Network	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Apparentées	5	4
Résumé	A Multilingual LSTM is a Long Short-Term Memory recurrent network trained or fine-tuned to process sequences in multiple languages, typically by sharing a single model across language-specific or joint subword embeddings. It captures long-range dependencies in text and is applied to multilingual classification, named entity recognition, sentiment analysis, and sequence labeling.	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.