  The emergence of large-scale pretrained language models has posed
unprecedented challenges in deriving explanations of why the model has made
some predictions. Stemmed from the compositional nature of languages, spurious
correlations have further undermined the trustworthiness of NLP systems,
leading to unreliable model explanations that are merely correlated with the
output predictions. To encourage fairness and transparency, there exists an
urgent demand for reliable explanations that allow users to consistently
understand the model's behavior. In this work, we propose a complete framework
for extending concept-based interpretability methods to NLP. Specifically, we
propose a post-hoc interpretability method for extracting predictive high-level
features (concepts) from the pretrained model's hidden layer activations. We
optimize for features whose existence causes the output predictions to change
substantially, \ie generates a high impact. Moreover, we devise several
evaluation metrics that can be universally applied. Extensive experiments on
real and synthetic tasks demonstrate that our method achieves superior results
on {predictive impact}, usability, and faithfulness compared to the baselines.
