  Pretrained transformer-based models such as BERT have demonstrated
state-of-the-art predictive performance when adapted into a range of natural
language processing tasks. An open problem is how to improve the faithfulness
of explanations (rationales) for the predictions of these models. In this
paper, we hypothesize that salient information extracted a priori from the
training data can complement the task-specific information learned by the model
during fine-tuning on a downstream task. In this way, we aim to help BERT not
to forget assigning importance to informative input tokens when making
predictions by proposing SaLoss; an auxiliary loss function for guiding the
multi-head attention mechanism during training to be close to salient
information extracted a priori using TextRank. Experiments for explanation
faithfulness across five datasets, show that models trained with SaLoss
consistently provide more faithful explanations across four different feature
attribution methods compared to vanilla BERT. Using the rationales extracted
from vanilla BERT and SaLoss models to train inherently faithful classifiers,
we further show that the latter result in higher predictive performance in
downstream tasks.
