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	Wielojęzyczne odpowiadanie na pytania	Wielojęzyczny transformator
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2018–2020	2019–2020
Twórca	Multiple groups; popularised via mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020)	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Typ	Extractive / generative QA across multiple languages	Pre-trained cross-lingual language model
Źródło pierwotne	Artetxe, M., Ruder, S., & Yogatama, D. (2020). On the cross-lingual transferability of monolingual representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4623–4637). 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.
Inne nazwy	cross-lingual question answering, multilingual QA, multilingual MRC, cross-lingual machine reading comprehension	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Pokrewne	4	4
Podsumowanie	Multilingual question answering (QA) enables a model to read a passage and answer questions in multiple languages, often by fine-tuning a cross-lingual pretrained transformer such as mBERT or XLM-R on an annotated QA dataset in one language and transferring that capability zero-shot or few-shot to other languages. It is the standard approach for building multilingual reading-comprehension and open-domain QA systems.	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.