在放射技术领域使用BERT回答日本填空题
Ayako Yagahara1, Noriya Yokoyama2, Mao Higashi3
1Hokkaido University of Science, Japan.
Studies in health technology and informatics
|January 25, 2024
概括
研究人员使用BERT模型评估了日本的放射技术填空问题. 一个原始模型的准确性与基于维基百科的模型相似,这表明使用更大的数据集有改进的潜力.
科学领域:
- 自然语言处理自然语言处理.
- 辐射技术 教育 辐射技术 教育
背景情况:
- 填空题在教育评估中很常见.
- 评估自动化问答系统的准确性对于教育技术至关重要.
- 伯特模型为自然语言理解任务提供了先进的功能.
研究的目的:
- 评估六个BERT模型在回答放射技术中日本填空问题的表现.
- 将一个定制训练的BERT模型与现有模型的准确性进行比较.
主要方法:
- 利用六个BERT模型来评估日本填空题.
- 训练了一个原始的BERT模型,使用33万个放射技术摘要.
- 将原始模型的准确性与在维基百科数据上训练的模型进行比较.
主要成果:
- 当词汇大小一致时,原来的BERT模型的准确性与基于维基百科的模型相美.
- 模型的性能受到培训数据集的大小和组成的直接影响.
结论:
- 在放射技术等专业领域,BERT模型对评估填空问题充满希望.
- 增加训练数据大小和结合日本维基百科等多种来源可以提高模型的准确性.
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