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| Multilingual Variational Autoencoder× | Rekurrente Neuronale Netze für Mehrsprachigkeit× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2017-2018 | 1990–2010s |
| Urheber≠ | Multiple research groups (Lample, Conneau et al.; Zhao et al.) | Elman, J. L. (RNN); multilingual extension by NLP community |
| Typ≠ | Generative latent-variable model | Sequential model (cross-lingual) |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. |
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