Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a majority vote), human-crafted free-form explanations can be quite subjective, as some recent works have discussed. Before blindly using them as ground truth to train ML models, a vital question needs to be asked: How do we evaluate a human-annotated explanation's quality? In this paper, we build on the view that the quality of a human-annotated explanation can be measured based on its helpfulness (or impairment) to the ML models' performance for the desired NLP tasks for which the annotations were collected. In comparison to the commonly used Simulatability score, we define a new metric that can take into consideration the helpfulness of an explanation for model performance at both fine-tuning and inference. With the help of a unified dataset format, we evaluated the proposed metric on five datasets (e.g., e-SNLI) against two model architectures (T5 and BART), and the results show that our proposed metric can objectively evaluate the quality of human-annotated explanations, while Simulatability falls short.

翻译：人工标注的标签和解释对于训练可解释的NLP模型至关重要。然而，与质量更易校准（例如通过多数投票）的人工标注标签不同，人工编写的自由形式解释可能相当主观，正如近期一些研究所讨论的那样。在盲目将其作为真实值用于训练机器学习模型之前，需要提出一个关键问题：如何评估人工标注解释的质量？本文基于以下观点：人工标注解释的质量可根据其对收集标注的目标NLP任务中ML模型性能的帮助（或损害）程度来衡量。与常用的可模拟性分数相比，我们定义了一个新指标，该指标可同时考虑解释在微调和推理阶段对模型性能的帮助。借助统一的数据集格式，我们在五个数据集（例如e-SNLI）上针对两种模型架构（T5和BART）评估了所提出的指标。结果表明，我们的指标能客观评估人工标注解释的质量，而可模拟性分数则存在不足。