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| 다국어 변이형 오토인코더× | 다국어 문장 임베딩× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017-2018 | 2019–2022 |
| 창시자≠ | Multiple research groups (Lample, Conneau et al.; Zhao et al.) | Reimers, N. & Gurevych, I.; Feng, F. et al. (Google) |
| 유형≠ | Generative latent-variable model | Cross-lingual representation learning |
| 원전≠ | Zhao, T., Zhang, Y., & Eskenazi, M. (2018). Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (pp. 1-10). ACL. link ↗ | Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗ |
| 별칭 | ML-VAE, cross-lingual VAE, multilingual latent variable model, multilingual generative autoencoder | multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings |
| 관련 | 5 | 5 |
| 요약≠ | A Multilingual Variational Autoencoder (ML-VAE) extends the standard VAE framework to handle multiple languages within a shared probabilistic latent space. Language-specific encoders map text from each language into a common continuous representation, while language-specific decoders reconstruct or translate that text. This enables cross-lingual generation, style transfer, and representation learning with or without parallel corpora. | 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. |
| ScholarGate데이터셋 ↗ |
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