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| 다국어 변이형 오토인코더× | 다국어 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017-2018 | 2019–2020 |
| 창시자≠ | Multiple research groups (Lample, Conneau et al.; Zhao et al.) | Devlin et al. (mBERT); Conneau et al. (XLM-R) |
| 유형≠ | Generative latent-variable model | Pre-trained cross-lingual language model |
| 원전≠ | Zhao, T., Zhang, Y., & Eskenazi, M. (2018). Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (pp. 1-10). ACL. link ↗ | 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 | ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder | multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model |
| 관련≠ | 5 | 4 |
| 요약≠ | A Multilingual Variational Autoencoder (ML-VAE) extends the standard VAE framework to handle multiple languages within a shared probabilistic latent space. Language-specific encoders map text from each language into a common continuous representation, while language-specific decoders reconstruct or translate that text. This enables cross-lingual generation, style transfer, and representation learning with or without parallel corpora. | 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. |
| ScholarGate데이터셋 ↗ |
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