方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 领域自适应问答× | 多语言问答× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019–2020 | 2018–2020 |
| 提出者≠ | Multiple (e.g., Garg et al.; Yue et al.) | Multiple groups; popularised via mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) |
| 类型≠ | Domain adaptation for extractive/generative QA | Extractive / generative QA across multiple languages |
| 开创性文献≠ | Garg, S., Vu, T., & Moschitti, A. (2020). TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7780–7788. DOI ↗ | 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 ↗ |
| 别名 | DA-QA, domain-adapted QA, domain-specific question answering, cross-domain question answering | cross-lingual question answering, multilingual QA, multilingual MRC, cross-lingual machine reading comprehension |
| 相关≠ | 6 | 4 |
| 摘要≠ | Domain-adaptive Question Answering (DA-QA) adapts a pre-trained language model — typically BERT or RoBERTa — first trained on general QA benchmarks such as SQuAD to answer questions accurately in a new target domain (e.g., biomedical, legal, financial) where labelled data is scarce. Combining domain-adaptive pre-training with task fine-tuning yields substantially stronger performance than direct fine-tuning alone. | 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. |
| ScholarGate数据集 ↗ |
|
|