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本地化基于变压器的生物医学语言模型的域内适应

Tommaso Mario Buonocore¹, Claudio Crema², Alberto Redolfi²

¹Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, 27100, Italy.

Journal of biomedical informatics

|June 29, 2023

概括

为意大利语等资源不足的语言创建生物医学语言模型是具有挑战性的. 这项研究发现,虽然数据量至关重要,但结合高质量的数据可以显著提高模型性能,即使资源有限.

科学领域:

计算语言学计算语言学
生物医学信息学是生物医学信息学.
自然语言处理自然语言处理.

背景情况:

数字医疗产生了大量的文本数据,对改善患者护理非常有价值.
对特定领域资源进行微调语言模型可以提高性能.
调整模型以适应意大利语等资源较少的语言的资源很少.

研究的目的:

研究可访问的方法来创建意大利生物医学语言模型.
在模型调整中,比较数据数量与质量的影响.
解决非英语医疗资源的域内适应差距.

主要方法:

探索了两种方法:神经机器翻译英语资源 (以数量为重点) 和使用本地意大利语语料库 (以质量为重点).
模型使用这些不同的数据集进行了微调.
绩效被评估以确定每个方法的有效性.

主要成果:

数据数量被证明是生物医学适应的数据质量比数据质量更重要的限制.
连接高质量的数据提高了模型性能,即使是在较小的公司.
开发的模型为意大利的医学研究和机构提供了潜力.

结论:

可以开发可访问的方法,以在资源较少的语言中创建生物医学语言模型.
平衡数据数量和质量是有效域调整的关键.
这些发现为将生物医学语言模型创建推广到其他语言和领域提供了洞察力.

关键词:

生物医学文本挖掘技术深度学习是一种深度学习.语言模型语言模型自然语言处理自然语言处理.变压器变压器变压器

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