  Language models (LMs) that jointly generate end-task answers as well as
free-text rationales are known as self-rationalization models. Recent works
demonstrate great performance gain for self-rationalization by few-shot
prompting LMs with rationale-augmented exemplars. However, the ability to
benefit from explanations only emerges with large-scale LMs, which have poor
accessibility. In this work, we explore the less-studied setting of leveraging
explanations for small LMs to improve few-shot self-rationalization. We first
revisit the relationship between rationales and answers. Inspired by the
implicit mental process of how human beings assess explanations, we present a
novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to
automatically construct pseudo-parallel data for self-training by reducing the
problem of plausibility judgement to natural language inference. Experimental
results show ZARA achieves SOTA performance on the FEB benchmark, for both the
task accuracy and the explanation metric. In addition, we conduct human and
quantitative evaluation validating ZARA's ability to automatically identify
plausible and accurate rationale-answer pairs.
