一种预期最大化方法,用于联合建模多维评级,该评级来自多个注释器
Anil Ramakrishna1, Rahul Gupta1, Ruth B Grossman2
1Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
概括
这项研究引入了一种新的算法来建模多维的人类评级,提高复杂任务的预测准确性,例如评估儿童表现力. 预期最大化方法有助于降低注释者的认知负载和成本.
科学领域:
- 机器学习 机器学习
- 人与计算机的交互
- 发展心理学 发展心理学
背景情况:
- 人类注释者评分对于各种应用中的地面真相估计至关重要.
- 这些评级通常是多维的和主观的.
- 现有的方法可能无法完全捕捉到多维评级的复杂性.
研究的目的:
- 提出基于预期最大化 (EM) 的算法,用于模拟多维人类评级.
- 从观察特征可靠地预测个人注释者评级.
- 为了应对注释者的认知负载和评级成本的挑战.
主要方法:
- 开发了一个EM算法,假设一个隐藏的多维基础真理.
- 训练了一个基线模型,用于直接预测注释者评分.
- 在三个设置下与基线进行比较:独立维度,联合分布和部分评级预测.
主要成果:
- 与直接基线相比,拟议的基于EM的模型证明了对多维评级的更好的预测.
- 在三个测试设置中,模型的性能各不相同,这凸显了考虑评级维度依赖性的重要性.
- 实现了对个人注释者评级的准确预测,验证了模型的有效性.
结论:
- 基于EM的算法为建模和预测多维人类评级提供了强大的框架.
- 这种方法对需要主观评估的应用有重大影响,例如在自闭症研究中评估儿童表现能力.
- 该模型为优化注释流程提供了一条途径,降低了成本和认知负担.
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