So sánh phương pháp

	Trả lời câu hỏi đa ngôn ngữ	Phân loại dựa trên BERT
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2018–2020	2019
Người khởi xướng	Multiple groups; popularised via mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Loại	Extractive / generative QA across multiple languages	Pre-trained language model with fine-tuning
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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Tên gọi khác	cross-lingual question answering, multilingual QA, multilingual MRC, cross-lingual machine reading comprehension	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Liên quan	4	4
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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.