方法对比
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| 领域自适应问答× | 微调问答× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019–2020 | 2016–2019 |
| 提出者≠ | Multiple (e.g., Garg et al.; Yue et al.) | Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark) |
| 类型≠ | Domain adaptation for extractive/generative QA | Transfer learning / fine-tuning for extractive or generative QA |
| 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 ↗ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. DOI ↗ |
| 别名 | DA-QA, domain-adapted QA, domain-specific question answering, cross-domain question answering | fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning |
| 相关≠ | 6 | 5 |
| 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. | Fine-Tuned Question Answering adapts a large pre-trained language model — such as BERT, RoBERTa, or a GPT-family model — to answer natural-language questions over a given context passage or knowledge base. The model learns to locate answer spans or generate free-form answers by continuing training on labeled QA pairs after general-purpose pre-training. |
| ScholarGate数据集 ↗ |
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