  A self-explaining rationalization model is generally constructed by a
cooperative game where a generator selects the most human-intelligible pieces
from the input text as rationales, followed by a predictor that makes
predictions based on the selected rationales. However, such a cooperative game
may incur the degeneration problem where the predictor overfits to the
uninformative pieces generated by a not yet well-trained generator and in turn,
leads the generator to converge to a sub-optimal model that tends to select
senseless pieces. In this paper, we theoretically bridge degeneration with the
predictor's Lipschitz continuity. Then, we empirically propose a simple but
effective method named DR, which can naturally and flexibly restrain the
Lipschitz constant of the predictor, to address the problem of degeneration.
The main idea of DR is to decouple the generator and predictor to allocate them
with asymmetric learning rates. A series of experiments conducted on two widely
used benchmarks have verified the effectiveness of the proposed method. Codes:
\href{https://github.com/jugechengzi/Rationalization-DR}{https://github.com/jugechengzi/Rationalization-DR}.
