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| 다국어 순환 신경망 (Multilingual Recurrent Neural Network)× | 다국어 LSTM× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1990–2010s | 1997 (LSTM); multilingual NLP applications from ~2016 |
| 창시자≠ | Elman, J. L. (RNN); multilingual extension by NLP community | Hochreiter, S. & Schmidhuber, J. (LSTM base); multilingual application by the NLP community from ~2016 |
| 유형≠ | Sequential model (cross-lingual) | Recurrent neural network (sequence model) |
| 원전≠ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| 별칭 | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN | Multilingual LSTM, Cross-lingual LSTM, Multi-language LSTM, Multilingual Recurrent Network |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
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