方法对比
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| 多语言多层感知机× | 微调多层感知机× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s | 1986 (MLP); fine-tuning practice formalised c. 2014 |
| 提出者≠ | McCulloch & Pitts / Rumelhart et al. (MLP); multilingual application became standard in NLP from the 2010s onward | Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis) |
| 类型≠ | Feedforward neural network (multilingual variant) | Supervised deep learning with pre-trained weight initialisation |
| 开创性文献≠ | Artetxe, M., & Schwartz, H. A. (2019). Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond. Transactions of the Association for Computational Linguistics, 7, 597–610. DOI ↗ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ |
| 别名 | Multilingual MLP, cross-lingual MLP, multilingual feedforward network, multilingual FFNN | fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning |
| 相关 | 4 | 4 |
| 摘要≠ | A Multilingual MLP is a feedforward neural network trained on text from two or more languages, relying on shared or aligned input representations — such as multilingual word embeddings or subword vocabularies — so that a single model can process and classify text across languages without separate per-language networks. | A Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce. |
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