There are different ways of explaining how machines make the decision (Ribeiro et al., 2016; Alvarez-Melis and Jaakkola, 2017;  Lee et al., 2019; Liu et al., 2019a), and one of these methods is to extract rationales  (Lei et al., 2016; DeYoung et al., 2019).
most prior work focused on extracting rationales in a supervised manner , but not all datasets contain such annotated rationales for model learning, making the rationalization task difficult and impractical.