Salīdzināt metodes

	Daudzvalodu Doc2Vec	Daudzvalodu teikumu iegulšanas
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2016	2019–2022
Autors	Le, Q. & Mikolov, T. (Doc2Vec); multilingual extension by community	Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)
Tips	Distributed document embedding (unsupervised / self-supervised)	Cross-lingual representation learning
Pirmavots	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.	Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.
Citi nosaukumi	multilingual paragraph vector, cross-lingual Doc2Vec, multilingual PV-DM, multilingual PV-DBOW	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
Saistītās	4	5
Kopsavilkums	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.