Data annotation is a costly task; thus, researchers have proposed
low-scenario learning techniques like Active-Learning (AL) to support human
annotators; Yet, existing AL works focus only on the label, but overlook the
natural language explanation of a data point, despite that real-world humans
(e.g., doctors) often need both the labels and the corresponding explanations
at the same time. This work proposes a novel AL architecture to support and
reduce human annotations of both labels and explanations in low-resource
scenarios. Our AL architecture incorporates an explanation-generation model
that can explicitly generate natural language explanations for the prediction
model and for assisting humans' decision-making in real-world. For our AL
framework, we design a data diversity-based AL data selection strategy that
leverages the explanation annotations. The automated AL simulation evaluations
demonstrate that our data selection strategy consistently outperforms
traditional data diversity-based strategy; furthermore, human evaluation
demonstrates that humans prefer our generated explanations to the SOTA
explanation-generation system.

该研究提出了一种新的主动学习框架，通过加入自然语言解释生成模型，实现在低资源条件下减少标签和解释注释的人工成本，并提升了决策方案生成的效果。