TPKE-QA: A gapless few-shot extractive question answering approach via task-aware post-training and knowledge enhancement

Author:

Xiao QiaoORCID,Li RenORCID,Yang Jianxi,Chen YuORCID,Jiang ShixinORCID,Wang Di

Funder

Natural Science Foundation of Chongqing Municipality

Chongqing Municipal Education Commission

Publisher

Elsevier BV

Reference64 articles.

1. BioASQ: A challenge on large-scale biomedical semantic indexing and question answering;Balikas;Revised Selected Papers from the First International Workshop on Multimodal Retrieval in the Medical Domain-Volume,2015

2. Language models are few-shot learners;Brown;Advances in Neural Information Processing Systems,2020

3. Chada, R., & Natarajan, P. (2021). FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 6081–6090.

4. Chen, X., Jiang, J.-Y., Chang, W.-C., Hsieh, C.-J., Yu, H.-F., & Wang, W. (2023). MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering. arXiv Preprint arXiv: 2310.05007.

5. Chen, X., Zhang, Y., Deng, J., Jiang, J.-Y., & Wang, W. (2023). Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 909–917.

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