  Neural network architectures in natural language processing often use
attention mechanisms to produce probability distributions over input token
representations. Attention has empirically been demonstrated to improve
performance in various tasks, while its weights have been extensively used as
explanations for model predictions. Recent studies (Jain and Wallace, 2019;
Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot
generally be considered as a faithful explanation (Jacovi and Goldberg, 2020)
across encoders and tasks. In this paper, we seek to improve the faithfulness
of attention-based explanations for text classification. We achieve this by
proposing a new family of Task-Scaling (TaSc) mechanisms that learn
task-specific non-contextualised information to scale the original attention
weights. Evaluation tests for explanation faithfulness, show that the three
proposed variants of TaSc improve attention-based explanations across two
attention mechanisms, five encoders and five text classification datasets
without sacrificing predictive performance. Finally, we demonstrate that TaSc
consistently provides more faithful attention-based explanations compared to
three widely-used interpretability techniques.
