使用NLP和swin变换器进行众包的真相推断方案
Ayswarya R Kurup1, Mithun Kumar Kar2, Somila Hashunao3
1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India. rk_ayswarya@cb.amrita.edu.
Scientific reports
|August 4, 2025
概括
本研究介绍了一种使用Swin变压器的新型真相推断模型,以提高众包数据的可靠性. 该方法在复杂的任务中提高了准确性和稳定性,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 自然语言处理自然语言处理.
背景情况:
- 众包提供可扩展的数据收集,但由于贡献者的专业知识和任务复杂性不同,在确保数据可靠性方面面临挑战.
- 真理推断对于从杂,异构的众包响应中获得准确答案至关重要.
- 现有的方法与众包数据的细微差别作斗争,影响推断真理的质量.
研究的目的:
- 提出一种新的真相推断模型,以提高众包数据的准确性和可靠性.
- 利用自然语言处理 (NLP) 和使用Swin变压器转移学习,以改进真相推断.
- 动态完善贡献者可靠性和任务难度估计,以获得更强大的结果.
主要方法:
- 整合自然语言处理 (NLP) 与使用Swin变换器的转移学习.
- 斯温变压器的转移窗口技术用于在文本数据中捕捉本地和全球上下文特征.
- 微调嵌入式表示,以满足众包任务的特定细微差别.
主要成果:
- 拟议的模型在准确性和可扩展性方面表现出高于最先进的方法的卓越性能.
- 在多个众包数据集中观察到一致的超越性,特别是在杂和复杂的条件下.
- 由于对贡献者的可靠性和任务难度的动态改进,在真相推断中提高了强度.
结论:
- 基于Swin变压器的真相推断模型显著提高了众包数据的质量和可靠性.
- 该方法为处理杂和复杂的众包任务提供了一个强大的解决方案.
- 这种方法代表了从众包平台获得准确信息的重大进步.
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