Linganisha mbinu

	Kigezo cha Kujifunza Kiotomatiki cha Tofauti cha Lugha Nyingi	Mtandao wa Nyuro wa Kujirudia wa Lugha Nyingi
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2017-2018	1990–2010s
Mwanzilishi	Multiple research groups (Lample, Conneau et al.; Zhao et al.)	Elman, J. L. (RNN); multilingual extension by NLP community
Aina	Generative latent-variable model	Sequential model (cross-lingual)
Chanzo asilia	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Majina mbadala	ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN
Zinazohusiana	5	5
Muhtasari	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.