방법 비교

	다국어 Doc2Vec	다국어 트랜스포머
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2014–2016	2019–2020
창시자	Le, Q. & Mikolov, T. (Doc2Vec); multilingual extension by community	Devlin et al. (mBERT); Conneau et al. (XLM-R)
유형	Distributed document embedding (unsupervised / self-supervised)	Pre-trained cross-lingual language model
원전	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.	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.
별칭	multilingual paragraph vector, cross-lingual Doc2Vec, multilingual PV-DM, multilingual PV-DBOW	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
관련	4	4
요약	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.	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.