Contrastive explanations, where one decision is explained in contrast to another, are supposed to be closer to how humans explain a decision than non-contrastive explanations, where the decision is not necessarily referenced to an alternative. This claim has never been empirically validated. We analyze four English text-classification datasets (SST2, DynaSent, BIOS and DBpedia-Animals). We fine-tune and extract explanations from three different models (RoBERTa, GTP-2, and T5), each in three different sizes and apply three post-hoc explainability methods (LRP, GradientxInput, GradNorm). We furthermore collect and release human rationale annotations for a subset of 100 samples from the BIOS dataset for contrastive and non-contrastive settings. A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans. Moreover, model-based explanations computed in both settings align equally well with human rationales. Thus, we empirically find that humans do not necessarily explain in a contrastive manner.9 pages, long paper at ACL 2022 proceedings.

翻译：对比解释（contrastive explanations）通过将某一决策与另一决策进行对比来解释，相较于非对比解释（non-contrastive explanations，即决策不必然参照替代方案），被认为更接近人类解释决策的方式。这一主张此前从未得到实证验证。我们分析了四个英文文本分类数据集（SST2、DynaSent、BIOS和DBpedia-Animals），对三种不同规模的模型（RoBERTa、GPT-2和T5）进行微调并提取解释，同时应用三种事后可解释性方法（LRP、Gradient×Input、GradNorm）。此外，我们从BIOS数据集中收集并发布了100个样本的人类原因注释，涵盖对比与非对比两种设置。在对比与非对比两种设置下，模型生成的原因与人类注释的交叉比较显示，模型与人类在两种设置之间均具有高度一致性。此外，两种设置下计算的模型解释与人类原因同样契合良好。因此，我们通过实证发现，人类并不必然以对比方式进行解释。本文为9页长文，收录于ACL 2022会议论文集。