Salīdzināt metodes

	Daudzvalodu tematu modelēšana	Daudzvalodu transformators
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2009	2019–2020
Autors	Mimno, D., Wallach, H. M., et al.	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Tips	Probabilistic topic model (multilingual extension)	Pre-trained cross-lingual language model
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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
Citi nosaukumi	cross-lingual topic model, polylingual LDA, multilingual LDA, MLTM	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Saistītās	5	4
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.	A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.