So sánh phương pháp

	Trả lời câu hỏi đa ngôn ngữ	Nhúng câu đa ngôn ngữ
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2018–2020	2019–2022
Người khởi xướng	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)
Loại	Extractive / generative QA across multiple languages	Cross-lingual representation learning
Công trình gốc	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.
Tên gọi khác	cross-lingual question answering, multilingual QA, multilingual MRC, cross-lingual machine reading comprehension	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
Liên quan	4	5
Tóm tắt	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.