  In the recent advances of natural language processing, the scale of the
state-of-the-art models and datasets is usually extensive, which challenges the
application of sample-based explanation methods in many aspects, such as
explanation interpretability, efficiency, and faithfulness. In this work, for
the first time, we can improve the interpretability of explanations by allowing
arbitrary text sequences as the explanation unit. On top of this, we implement
a hessian-free method with a model faithfulness guarantee. Finally, to compare
our method with the others, we propose a semantic-based evaluation metric that
can better align with humans' judgment of explanations than the widely adopted
diagnostic or re-training measures. The empirical results on multiple real data
sets demonstrate the proposed method's superior performance to popular
explanation techniques such as Influence Function or TracIn on semantic
evaluation.
