@inproceedings{ca00f849155b4550b9e8b1d7aa2d2c25,
title = "Multi-task Question Generation Based Data Augmentation for Biomedical Answer Generation",
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.",
keywords = "Biomedical Answer Generation, Data Augmentation, Multi-task Learning",
author = "Junting Zhao and Jun Bai and Wenge Rong and Yuanxin Ouyang and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 19th International Conference on Intelligent Computing, ICIC 2023 ; Conference date: 10-08-2023 Through 13-08-2023",
year = "2023",
doi = "10.1007/978-981-99-4749-2\_41",
language = "英语",
isbn = "9789819947485",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "485--496",
editor = "De-Shuang Huang and Prashan Premaratne and Baohua Jin and Boyang Qu and Kang-Hyun Jo and Abir Hussain",
booktitle = "Advanced Intelligent Computing Technology and Applications - 19th International Conference, ICIC 2023, Proceedings",
address = "德国",
}