  We present a novel rationale-centric framework with human-in-the-loop --
Rationales-centric Double-robustness Learning (RDL) -- to boost model
out-of-distribution performance in few-shot learning scenarios. By using static
semi-factual generation and dynamic human-intervened correction, RDL exploits
rationales (i.e. phrases that cause the prediction), human interventions and
semi-factual augmentations to decouple spurious associations and bias models
towards generally applicable underlying distributions, which enables fast and
accurate generalisation. Experimental results show that RDL leads to
significant prediction benefits on both in-distribution and out-of-distribution
tests compared to many state-of-the-art benchmarks -- especially for few-shot
learning scenarios. We also perform extensive ablation studies to support
in-depth analyses of each component in our framework.
