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| 다국어 순환 신경망 (Multilingual Recurrent Neural Network)× | 다국어 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1990–2010s | 2019–2020 |
| 창시자≠ | Elman, J. L. (RNN); multilingual extension by NLP community | Devlin et al. (mBERT); Conneau et al. (XLM-R) |
| 유형≠ | Sequential model (cross-lingual) | Pre-trained cross-lingual language model |
| 원전≠ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ | 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. DOI ↗ |
| 별칭 | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN | multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | 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. |
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