Machine learningDeep learning / NLP / CV
多语言扩散模型
多语言扩散模型（Multilingual Diffusion Model）调整了去噪扩散概率框架，使其能够跨多种语言工作，从而实现跨语言文本生成、翻译和与语言无关的内容合成。通过以多语言表示为条件，扩散过程学习了一个跨越语言边界的共享潜在空间，为低资源和高资源语言生成高质量的输出。
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来源
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗
- Gong, S., Li, M., Feng, J., Wu, Z., & Kong, L. (2023). DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. International Conference on Learning Representations (ICLR). link ↗
如何引用本页
ScholarGate. (2026, June 3). Multilingual Diffusion Model for Text and Cross-Lingual Generation. ScholarGate. https://scholargate.app/zh/deep-learning/multilingual-diffusion-model
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