Salīdzināt metodes

	Daudzvalodu jautājumu atbildēšana	Daudzvalodu teikumu iegulšanas
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018–2020	2019–2022
Autors	Multiple groups; popularised via mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020)	Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)
Tips	Extractive / generative QA across multiple languages	Cross-lingual representation learning
Pirmavots	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.	Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.
Citi nosaukumi	cross-lingual question answering, multilingual QA, multilingual MRC, cross-lingual machine reading comprehension	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
Saistītās	4	5
Kopsavilkums	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.	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.