  Many recent deep learning-based solutions have widely adopted the
attention-based mechanism in various tasks of the NLP discipline. However, the
inherent characteristics of deep learning models and the flexibility of the
attention mechanism increase the models' complexity, thus leading to challenges
in model explainability. In this paper, to address this challenge, we propose a
novel practical framework by utilizing a two-tier attention architecture to
decouple the complexity of explanation and the decision-making process. We
apply it in the context of a news article classification task. The experiments
on two large-scaled news corpora demonstrate that the proposed model can
achieve competitive performance with many state-of-the-art alternatives and
illustrate its appropriateness from an explainability perspective.
