Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction. A typical rationalization criterion, i.e. maximum mutual information (MMI), finds the rationale that maximizes the prediction performance based only on the rationale. However, MMI can be problematic because it picks up spurious correlations between the input features and the output. Instead, we introduce a game-theoretic invariant rationalization criterion where the rationales are constrained to enable the same predictor to be optimal across different environments. We show both theoretically and empirically that the proposed rationales can rule out spurious correlations, generalize better to different test scenarios, and align better with human judgments. Our data and code are available.

中文翻译：

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不变的合理化

选择性合理化通过识别最能解释或支持预测的输入特征的一小部分子集——基本原理——来提高神经网络的可解释性。一个典型的合理化标准，即最大互信息（MMI），仅根据基本原理来寻找使预测性能最大化的基本原理。然而，MMI 可能存在问题，因为它会检测输入特征和输出之间的虚假相关性。相反，我们引入了一个博弈论不变合理化标准，其中基本原理被限制为使相同的预测器能够在不同的环境中达到最佳。我们从理论上和经验上都表明，所提出的基本原理可以排除虚假相关性，更好地推广到不同的测试场景，并更好地与人类判断保持一致。