  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.
