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| Wielojęzyczne LSTM× | Długa pamięć krótkotrwała (LSTM)× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1997 (LSTM); multilingual NLP applications from ~2016 | 1997 |
| Twórca≠ | Hochreiter, S. & Schmidhuber, J. (LSTM base); multilingual application by the NLP community from ~2016 | Hochreiter, S. & Schmidhuber, J. |
| Typ≠ | Recurrent neural network (sequence model) | Recurrent neural network with gated memory cells |
| Źródło pierwotne | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Inne nazwy | Multilingual LSTM, Cross-lingual LSTM, Multi-language LSTM, Multilingual Recurrent Network | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | 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. |
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