Salīdzināt metodes

	Daudzvalodu tematu modelēšana	Daudzvalodu teikumu iegulšanas
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2009	2019–2022
Autors	Mimno, D., Wallach, H. M., et al.	Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)
Tips	Probabilistic topic model (multilingual extension)	Cross-lingual representation learning
Pirmavots	Mimno, D., Wallach, H. M., Naradowsky, J., Smith, D. A., & McCallum, A. (2009). Polylingual topic models. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 880–889. ACL.	Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.
Citi nosaukumi	cross-lingual topic model, polylingual LDA, multilingual LDA, MLTM	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
Saistītās	5	5
Kopsavilkums	Multilingual topic modeling extends probabilistic topic models such as LDA to corpora spanning two or more languages, inferring shared latent topics across language boundaries. By tying topic distributions across languages, it enables cross-lingual document analysis, comparable topic discovery, and information retrieval without requiring full 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.