  Explaining black-box model behavior with natural language has achieved
impressive results in various NLP tasks. Recent research has explored the
utilization of subsequences from the input text as a rationale, providing users
with evidence to support the model decision. Although existing frameworks excel
in generating high-quality rationales while achieving high task performance,
they neglect to account for the unreliable link between the generated rationale
and model decision. In simpler terms, a model may make correct decisions while
attributing wrong rationales, or make poor decisions while attributing correct
rationales. To mitigate this issue, we propose a unified two-stage framework
known as Self-Attribution and Decision-Making (SADM). Through extensive
experiments on five reasoning datasets from the ERASER benchmark, we
demonstrate that our framework not only establishes a more reliable link
between the generated rationale and model decision but also achieves
competitive results in task performance and the quality of rationale.
Furthermore, we explore the potential of our framework in semi-supervised
scenarios.
