Sammenlign metoder

	Flersproget Convolutional Neural Network	Multilingual Transformer
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2014–2016	2019–2020
Ophavsperson	Kim, Y. (seminal NLP CNN); multilingual extension by community	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Type	Deep learning classifier	Pre-trained cross-lingual language model
Oprindelig kilde	Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of EMNLP 2014, pp. 1746–1751.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
Aliasser	ML-CNN, cross-lingual CNN, multilingual text CNN, multilingual ConvNet	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Relaterede	4	4
Resumé	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 transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.