  Rationalization is to employ a generator and a predictor to construct a
self-explaining NLP model in which the generator selects a subset of
human-intelligible pieces of the input text to the following predictor.
However, rationalization suffers from two key challenges, i.e., spurious
correlation and degeneration, where the predictor overfits the spurious or
meaningless pieces solely selected by the not-yet well-trained generator and in
turn deteriorates the generator. Although many studies have been proposed to
address the two challenges, they are usually designed separately and do not
take both of them into account. In this paper, we propose a simple yet
effective method named MGR to simultaneously solve the two problems. The key
idea of MGR is to employ multiple generators such that the occurrence stability
of real pieces is improved and more meaningful pieces are delivered to the
predictor. Empirically, we show that MGR improves the F1 score by up to 20.9%
as compared to state-of-the-art methods. Codes are available at
https://github.com/jugechengzi/Rationalization-MGR .

Machine Learning;Computation and Language