  The remarkable success in neural networks provokes the selective
rationalization. It explains the prediction results by identifying a small
subset of the inputs sufficient to support them. Since existing methods still
suffer from adopting the shortcuts in data to compose rationales and limited
large-scale annotated rationales by human, in this paper, we propose a
Shortcuts-fused Selective Rationalization (SSR) method, which boosts the
rationalization by discovering and exploiting potential shortcuts.
Specifically, SSR first designs a shortcuts discovery approach to detect
several potential shortcuts. Then, by introducing the identified shortcuts, we
propose two strategies to mitigate the problem of utilizing shortcuts to
compose rationales. Finally, we develop two data augmentations methods to close
the gap in the number of annotated rationales. Extensive experimental results
on real-world datasets clearly validate the effectiveness of our proposed
method.
