Compară metode

	Rețea Neuronală Convoluțională Multilingvă	Rețea Recurentă Multilingvă
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2014–2016	1990–2010s
Autorul original	Kim, Y. (seminal NLP CNN); multilingual extension by community	Elman, J. L. (RNN); multilingual extension by NLP community
Tip	Deep learning classifier	Sequential model (cross-lingual)
Sursa seminală	Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of EMNLP 2014, pp. 1746–1751.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Denumiri alternative	ML-CNN, cross-lingual CNN, multilingual text CNN, multilingual ConvNet	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN
Înrudite	4	5
Rezumat	A Multilingual CNN applies convolutional filters over token embeddings drawn from two or more languages, producing shared feature representations that enable a single model to classify, tag, or extract information across language boundaries without training separate models per language. It extends the standard text-CNN architecture with multilingual or cross-lingual input embeddings.	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.