  Despite large-scale pre-trained language models have achieved striking
results for text classificaion, recent work has raised concerns about the
challenge of shortcut learning. In general, a keyword is regarded as a shortcut
if it creates a superficial association with the label, resulting in a false
prediction. Conversely, shortcut learning can be mitigated if the model relies
on robust causal features that help produce sound predictions. To this end,
many studies have explored post-hoc interpretable methods to mine shortcuts and
causal features for robustness and generalization. However, most existing
methods focus only on single word in a sentence and lack consideration of
word-group, leading to wrong causal features. To solve this problem, we propose
a new Word-Group mining approach, which captures the causal effect of any
keyword combination and orders the combinations that most affect the
prediction. Our approach bases on effective post-hoc analysis and beam search,
which ensures the mining effect and reduces the complexity. Then, we build a
counterfactual augmentation method based on the multiple word-groups, and use
an adaptive voting mechanism to learn the influence of different augmentated
samples on the prediction results, so as to force the model to pay attention to
effective causal features. We demonstrate the effectiveness of the proposed
method by several tasks on 8 affective review datasets and 4 toxic language
datasets, including cross-domain text classificaion, text attack and gender
fairness test.
