Machine learningDeep learning / NLP / CV
Multilingual Sentence Embeddings
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.
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Sources
- Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗
- Feng, F., Yang, Y., Cer, D., Arivazhagan, N. & Wang, W. (2022). Language-agnostic BERT Sentence Embedding. Proceedings of ACL 2022, 878–891. DOI: 10.18653/v1/2022.acl-long.62 ↗
Related methods
Referenced by
Domain-adaptive sentence embeddingsMultilingual Diffusion ModelMultilingual Doc2VecMultilingual GANMultilingual graph neural networkMultilingual Image ClassificationMultilingual LSTMMultilingual Multilayer PerceptronMultilingual question answeringMultilingual Reinforcement LearningMultilingual RoBERTa-based ClassificationMultilingual Sentiment AnalysisMultilingual topic modelingMultilingual TransformerMultilingual variational autoencoderMultilingual vision transformer