  Recent studies strive to incorporate various human rationales into neural
networks to improve model performance, but few pay attention to the quality of
the rationales. Most existing methods distribute their models' focus to
distantly-labeled rationale words entirely and equally, while ignoring the
potential important non-rationale words and not distinguishing the importance
of different rationale words. In this paper, we propose two novel auxiliary
loss functions to make better use of distantly-labeled rationales, which
encourage models to maintain their focus on important words beyond labeled
rationales (PINs) and alleviate redundant training on non-helpful rationales
(NoIRs). Experiments on two representative classification tasks show that our
proposed methods can push a classification model to effectively learn crucial
clues from non-perfect rationales while maintaining the ability to spread its
focus to other unlabeled important words, thus significantly outperform
existing methods.
