Abstract
Limited by the corpus size and the annotation cost, biomedical question answering (BioQA) is a task of great research value. To generate professional biomedical answers, we first propose a text-to-text multi-task question generation model, which improves the accuracy of domain question generation with two auxiliary tasks. Based on this, a multi-task QA pipeline system with filtering is designed to synthesize high-quality biomedical data. Then, we use three data augmentation strategies to conduct generative BioQA experiments on original and synthetic data. The results on the factoid BioASQ 7b, 8b, and 9b datasets demonstrate the effectiveness of our method.
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This work was partially supported by the National Natural Science Foundation of China (No. 61977002).
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Zhao, J., Bai, J., Rong, W., Ouyang, Y., Xiong, Z. (2023). Multi-task Question Generation Based Data Augmentation for Biomedical Answer Generation. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_41
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DOI: https://doi.org/10.1007/978-981-99-4749-2_41
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