论文标题
以人为基础的人类机器学习的基本原理框架
A Rationale-Centric Framework for Human-in-the-loop Machine Learning
论文作者
论文摘要
我们提出了一个新颖的以理由理由为中心的框架，其中包括人类的人类 - 以理性为中心的双重运动学习（RDL） - 在几乎没有研究的方案中促进模型过分分发性能。通过使用静态的半事实产生和动态的人性性校正，RDL利用了理由（即导致预测的短语），人干预措施和半事实的增强，以将潮流的关联和偏见模型降低到普遍适用的基础分布，从而实现快速而准确的常规化。实验结果表明，与许多最先进的基准相比，RDL会导致分布和分布测试的显着预测益处 - 尤其是对于很少的学习方案。我们还进行了广泛的消融研究，以支持框架中每个组件的深入分析。
We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks -- especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework.
