Rationalization empowers deep learning models with self-explaining capabilities through a cooperative game, where a generator selects a semantically consistent subset of the input as a rationale, and a subsequent predictor makes predictions based on the selected rationale. In this paper, we discover that rationalization is prone to a problem named \emph{rationale shift}, which arises from the algorithmic bias of the cooperative game. Rationale shift refers to a situation where the semantics of the selected rationale may deviate from the original input, but the predictor still produces accurate predictions based on the deviation, resulting in a compromised generator with misleading feedback. To address this issue, we first demonstrate the importance of the alignment between the rationale and the full input through both empirical observations and theoretical analysis. Subsequently, we introduce a novel approach called DAR (\textbf{D}iscriminatively \textbf{A}ligned \textbf{R}ationalization), which utilizes an auxiliary module pretrained on the full input to discriminatively align the selected rationale and the original input. We theoretically illustrate how DAR accomplishes the desired alignment, thereby overcoming the rationale shift problem. The experiments on two widely used real-world benchmarks show that the proposed method significantly improves the explanation quality (measured by the overlap between the model-selected explanation and the human-annotated rationale) as compared to state-of-the-art techniques. Additionally, results on two synthetic settings further validate the effectiveness of DAR in addressing the rationale shift problem.

翻译：理性化通过协作博弈赋予深度学习模型自解释能力：生成器选择输入中语义一致的子集作为理由，随后预测器基于所选理由进行预测。本文发现理性化易受名为“理由偏移”的问题影响，该问题源于协作博弈的算法偏差。理由偏移指所选理由的语义可能偏离原始输入，但预测器仍能基于这种偏差做出准确预测，导致生成器因误导性反馈而性能受损。为解决此问题，我们首先通过实证观察和理论分析证明了理由与完整输入之间对齐的重要性。随后提出一种名为DAR（判别式对齐理性化）的新方法，该方法利用在完整输入上预训练的辅助模块，判别性地对齐所选理由与原始输入。我们从理论上阐述了DAR如何实现期望的对齐，从而克服理由偏移问题。在两个广泛使用的真实世界基准上的实验表明，与最先进技术相比，所提方法显著提升了解释质量（通过模型选择解释与人工标注理由的重叠度衡量）。此外，在两个合成场景上的结果进一步验证了DAR在解决理由偏移问题上的有效性。