方法对比

	多语言 Transformer	[需翻译标题：BERT-based Classification...]
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019–2020	2019
提出者	Devlin et al. (mBERT); Conneau et al. (XLM-R)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
类型	Pre-trained cross-lingual language model	Pre-trained language model with fine-tuning
开创性文献	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
别名	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
相关	4	4
摘要	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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.