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| 多语言图神经网络× | 多语言句子嵌入× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2019–2022 |
| 提出者≠ | Various (Kipf & Welling 2017 for GNN; multilingual extensions from NLP community ~2019) | Reimers, N. & Gurevych, I.; Feng, F. et al. (Google) |
| 类型≠ | Graph-based deep learning with multilingual node/edge features | Cross-lingual representation learning |
| 开创性文献≠ | Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR 2017. link ↗ | Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗ |
| 别名 | Multilingual GNN, cross-lingual GNN, multilingual graph network, multilingual relational GNN | multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings |
| 相关 | 5 | 5 |
| 摘要≠ | A Multilingual Graph Neural Network (Multilingual GNN) applies graph-based message-passing over nodes and edges that carry features from two or more languages. It is used for tasks such as cross-lingual entity alignment, multilingual knowledge-graph completion, and relation extraction across parallel or comparable corpora, allowing structural and semantic information from multiple languages to be jointly learned. | Multilingual sentence embeddings map sentences from many languages into a single shared vector space so that semantically equivalent sentences — regardless of language — land close together. Models such as LaBSE, multilingual Sentence-BERT, and mUSE have made it practical to compare, retrieve, and classify text across 50 to 100+ languages without translating anything first. |
| ScholarGate数据集 ↗ |
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