Сравнение на методи

	Многоезична конволюционна невронна мрежа	Многоезична рекурентна невронна мрежа
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2014–2016	1990–2010s
Създател	Kim, Y. (seminal NLP CNN); multilingual extension by community	Elman, J. L. (RNN); multilingual extension by NLP community
Тип	Deep learning classifier	Sequential model (cross-lingual)
Основополагащ източник	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.
Други названия	ML-CNN, cross-lingual CNN, multilingual text CNN, multilingual ConvNet	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN
Свързани	4	5
Резюме	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.