方法对比
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| 多语言多层感知机× | 多语言循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s | 1990–2010s |
| 提出者≠ | McCulloch & Pitts / Rumelhart et al. (MLP); multilingual application became standard in NLP from the 2010s onward | Elman, J. L. (RNN); multilingual extension by NLP community |
| 类型≠ | Feedforward neural network (multilingual variant) | Sequential model (cross-lingual) |
| 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 ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | Multilingual MLP, cross-lingual MLP, multilingual feedforward network, multilingual FFNN | Multilingual RNN, Cross-lingual RNN, Multi-language RNN, MRNN |
| 相关≠ | 4 | 5 |
| 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 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. |
| ScholarGate数据集 ↗ |
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