Methoden vergelijken

	Multilinguale Graph Neurale Netwerken	Meertalige Zins-Embeddings
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2019	2019–2022
Grondlegger	Various (Kipf & Welling 2017 for GNN; multilingual extensions from NLP community ~2019)	Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)
Type	Graph-based deep learning with multilingual node/edge features	Cross-lingual representation learning
Oorspronkelijke bron	Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR 2017.	Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.
Aliassen	Multilingual GNN, cross-lingual GNN, multilingual graph network, multilingual relational GNN	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
Verwant	5	5
Samenvatting	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.