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| 多语言扩散模型× | 多语言句子嵌入× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020–2023 | 2019–2022 |
| 提出者≠ | Ho, J., Jain, A., & Abbeel, P. (diffusion foundation); multilingual NLP extensions by various authors (2022–2024) | Reimers, N. & Gurevych, I.; Feng, F. et al. (Google) |
| 类型≠ | Generative model (denoising diffusion process, multilingual extension) | Cross-lingual representation learning |
| 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 ↗ | Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗ |
| 别名 | Multilingual DiffuSeq, Cross-lingual Diffusion Model, Multilingual DDPM, Multilingual Denoising Diffusion | multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings |
| 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. | Multilingual sentence embeddings map sentences from many languages into a single shared vector space so that semantically equivalent sentences — regardless of language — land close together. Models such as LaBSE, multilingual Sentence-BERT, and mUSE have made it practical to compare, retrieve, and classify text across 50 to 100+ languages without translating anything first. |
| ScholarGate数据集 ↗ |
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