Towards Faithful Explanations for Text Classification with Robustness
  Improvement and Explanation Guided Training
Dongfang Li Baotian Hu Qingcai Chen Shan He
Feature attribution methods highlight the important input tokens as
explanations to model predictions, which have been widely applied to deep
neural networks towards trustworthy AI. However, recent works show that
explanations provided by these methods face challenges of being faithful and
robust. In this paper, we propose a method with Robustness improvement and
Explanation Guided training towards more faithful EXplanations (REGEX) for text
classification. First, we improve model robustness by input gradient
regularization technique and virtual adversarial training. Secondly, we use
salient ranking to mask noisy tokens and maximize the similarity between model
attention and feature attribution, which can be seen as a self-training
procedure without importing other external information. We conduct extensive
experiments on six datasets with five attribution methods, and also evaluate
the faithfulness in the out-of-domain setting. The results show that REGEX
improves fidelity metrics of explanations in all settings and further achieves
consistent gains based on two randomization tests. Moreover, we show that using
highlight explanations produced by REGEX to train select-then-predict models
results in comparable task performance to the end-to-end method.