论文标题
引起注意的新颖观点：基于双层注意的新闻分类的可解释的主题建模
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
论文作者
论文摘要
在NLP学科的各种任务中，许多最近的基于深度学习的解决方案已广泛采用了基于注意力的机制。但是，深度学习模型的固有特征和注意机制的灵活性增加了模型的复杂性，从而导致了模型解释性的挑战。在本文中，为了应对这一挑战，我们提出了一个新颖的实践框架，它利用两层注意体系结构将解释和决策过程的复杂性解散。我们在新闻文章分类任务的背景下应用它。对两个大型新闻机构的实验表明，提出的模型可以通过许多最先进的替代方案来实现竞争性能，并从解释性的角度说明其适当性。
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.
