  Explanation is important for text classification tasks. One prevalent type of
explanation is rationales, which are text snippets of input text that suffice
to yield the prediction and are meaningful to humans. A lot of research on
rationalization has been based on the selective rationalization framework,
which has recently been shown to be problematic due to the interlocking
dynamics. In this paper, we show that we address the interlocking problem in
the multi-aspect setting, where we aim to generate multiple rationales for
multiple outputs. More specifically, we propose a multi-stage training method
incorporating an additional self-supervised contrastive loss that helps to
generate more semantically diverse rationales. Empirical results on the beer
review dataset show that our method improves significantly the rationalization
performance.
