方法对比
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| 多语言变分自编码器× | 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≠ | 2017-2018 | 1990–2010s |
| 提出者≠ | Multiple research groups (Lample, Conneau et al.; Zhao et al.) | Elman, J. L. (RNN); multilingual extension by NLP community |
| 类型≠ | Generative latent-variable model | Sequential model (cross-lingual) |
| 开创性文献≠ | Zhao, T., Zhang, Y., & Eskenazi, M. (2018). Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (pp. 1-10). ACL. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN |
| 相关 | 5 | 5 |
| 摘要≠ | A Multilingual Variational Autoencoder (ML-VAE) extends the standard VAE framework to handle multiple languages within a shared probabilistic latent space. Language-specific encoders map text from each language into a common continuous representation, while language-specific decoders reconstruct or translate that text. This enables cross-lingual generation, style transfer, and representation learning with or without parallel corpora. | 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. |
| ScholarGate数据集 ↗ |
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