方法对比
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| 多语言循环神经网络× | 循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1990–2010s | 1986–1990 |
| 提出者≠ | Elman, J. L. (RNN); multilingual extension by NLP community | Rumelhart, D. E.; Elman, J. L. |
| 类型≠ | Sequential model (cross-lingual) | Sequential neural network |
| 开创性文献 | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN | RNN, Elman network, Jordan network, simple recurrent network |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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 Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
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