Sammenlign metoder

	Multilingual GRU	Multilingual Recurrent Neural Network
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2014 (GRU); multilingual applications from ~2016	1990–2010s
Ophavsperson	Cho, K. et al. (GRU); multilingual extension by NLP community	Elman, J. L. (RNN); multilingual extension by NLP community
Type	Recurrent sequence model (multilingual)	Sequential model (cross-lingual)
Oprindelig kilde	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Aliasser	Multilingual GRU, cross-lingual GRU, multilingual gated recurrent unit, multi-language GRU	Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN
Relaterede	4	5
Resumé	A Multilingual GRU is a Gated Recurrent Unit network trained on text data spanning multiple languages, enabling sequential modeling of language-sensitive tasks such as sentiment analysis, named entity recognition, and machine translation across language boundaries without requiring separate models per language.	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.