方法证据记录
Multilingual Diffusion Model
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.
源记录
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Multilingual Diffusion Model for Text and Cross-Lingual Generation
分类方法记录 · ml-model / deep-learning
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. · URL
- 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). · URL
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