Machine learningDeep learning / NLP / CV
Domain-adaptive Question Answering
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- 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: 10.1609/aaai.v34i05.6282 ↗
- Yue, X., Zeng, Z., Shi, Y., Zhang, C., & Song, Y. (2022). Domain-adaptive Pre-training Methods for Natural Language Understanding. arXiv preprint. link ↗