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| 다국어 GAN× | 다국어 순환 신경망 (Multilingual Recurrent Neural Network)× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2019 | 1990–2010s |
| 창시자≠ | Goodfellow et al. (GAN); multilingual extensions by various authors from 2017 onward | Elman, J. L. (RNN); multilingual extension by NLP community |
| 유형≠ | Generative adversarial model with multilingual conditioning | Sequential model (cross-lingual) |
| 원전≠ | Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 별칭 | Multilingual GAN, Cross-lingual GAN, Multilingual Generative Adversarial Network, ML-GAN | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN |
| 관련 | 5 | 5 |
| 요약≠ | A Multilingual GAN pairs the generative adversarial framework with cross-lingual components — a shared encoder, language-conditioned generator, and a language discriminator — so that a single model can generate or align representations across multiple languages simultaneously. It is applied to cross-lingual text generation, machine translation, multilingual data augmentation, and language-invariant feature learning. | 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. |
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