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| 다국어 질의응답× | 다국어 문장 임베딩× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018–2020 | 2019–2022 |
| 창시자≠ | 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) |
| 유형≠ | Extractive / generative QA across multiple languages | Cross-lingual representation learning |
| 원전≠ | 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. DOI ↗ | Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525. link ↗ |
| 별칭 | cross-lingual question answering, multilingual QA, multilingual MRC, cross-lingual machine reading comprehension | multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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