Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Mitmekeelne Doc2Vec× | Mitmekeelsed lausungmanused× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2014–2016 | 2019–2022 |
| Looja≠ | Le, Q. & Mikolov, T. (Doc2Vec); multilingual extension by community | Reimers, N. & Gurevych, I.; Feng, F. et al. (Google) |
| Tüüp≠ | Distributed document embedding (unsupervised / self-supervised) | Cross-lingual representation learning |
| Algallikas≠ | Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2), 1188–1196. link ↗ | Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗ |
| Rööpnimetused | multilingual paragraph vector, cross-lingual Doc2Vec, multilingual PV-DM, multilingual PV-DBOW | multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings |
| Seotud≠ | 4 | 5 |
| Kokkuvõte≠ | Multilingual Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) to two or more languages, training document-level embeddings in a shared or aligned vector space so that semantically similar documents — regardless of their language — end up close together. It enables cross-lingual document retrieval, classification, and clustering without requiring parallel corpora or translation. | 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. |
| ScholarGateAndmestik ↗ |
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