方法对比
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| 多语言扩散模型× | 多语言循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020–2023 | 1990–2010s |
| 提出者≠ | Ho, J., Jain, A., & Abbeel, P. (diffusion foundation); multilingual NLP extensions by various authors (2022–2024) | Elman, J. L. (RNN); multilingual extension by NLP community |
| 类型≠ | Generative model (denoising diffusion process, multilingual extension) | Sequential model (cross-lingual) |
| 开创性文献≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | Multilingual DiffuSeq, Cross-lingual Diffusion Model, Multilingual DDPM, Multilingual Denoising Diffusion | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN |
| 相关 | 5 | 5 |
| 摘要≠ | A Multilingual Diffusion Model adapts the denoising diffusion probabilistic framework to work across multiple languages, enabling cross-lingual text generation, translation, and language-agnostic content synthesis. By conditioning on multilingual representations, the diffusion process learns a shared latent space that spans linguistic boundaries, producing high-quality outputs for low- and high-resource languages alike. | 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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