方法对比
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| 多语言卷积神经网络× | 多语言 Transformer× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014–2016 | 2019–2020 |
| 提出者≠ | Kim, Y. (seminal NLP CNN); multilingual extension by community | Devlin et al. (mBERT); Conneau et al. (XLM-R) |
| 类型≠ | Deep learning classifier | Pre-trained cross-lingual language model |
| 开创性文献≠ | Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of EMNLP 2014, pp. 1746–1751. 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 ↗ |
| 别名 | ML-CNN, cross-lingual CNN, multilingual text CNN, multilingual ConvNet | multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model |
| 相关 | 4 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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