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| Tóm tắt văn bản đa ngôn ngữ× | Phân loại dựa trên RoBERTa đa ngôn ngữ× | |
|---|---|---|
| Lĩnh vực | Học sâu | Học sâu |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2020–2021 | 2020 |
| Người khởi xướng≠ | Multiple groups; popularized via mBART (Liu et al., 2020) and mT5 (Xue et al., 2021) | Conneau, A. et al. (Facebook AI Research) |
| Loại≠ | Seq2seq / encoder-decoder fine-tuning for summarization across languages | Pretrained multilingual transformer fine-tuned for classification |
| Công trình gốc≠ | Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021). mT5: A Massively Multilingual Pre-Trained Text-to-Text Transformer. Proceedings of NAACL-HLT 2021, pp. 483–498. Association for Computational Linguistics. link ↗ | Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzman, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 8440–8451. DOI ↗ |
| Tên gọi khác | cross-lingual summarization, multilingual abstractive summarization, multilingual extractive summarization, multilingual seq2seq summarization | XLM-RoBERTa classification, mRoBERTa, cross-lingual RoBERTa classifier, multilingual transformer classification |
| Liên quan | 4 | 4 |
| Tóm tắt≠ | Multilingual text summarization applies pre-trained multilingual encoder-decoder models — such as mT5 or mBART — to generate concise summaries of documents written in many languages, either within the same language (monolingual) or across languages (cross-lingual). Fine-tuning these models on multilingual summarization benchmarks like XL-Sum enables coverage of dozens of languages with a single model. | Multilingual RoBERTa-based classification uses XLM-RoBERTa — a transformer pretrained on 100+ languages via masked language modeling — and fine-tunes it on labeled text to assign categories across multiple languages. By sharing a single model across languages, it enables robust cross-lingual and zero-shot text classification without needing separate per-language classifiers. |
| ScholarGateBộ dữ liệu ↗ |
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