论文标题
学习差异词蒙版，以提高神经文本分类器的可解释性
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers
论文作者
论文摘要
为了构建可解释的神经文本分类器，大多数先前的工作都集中在设计固有的可解释模型或寻找忠实的解释上。一项有关改进模型可解释性的新工作刚刚开始，许多现有方法需要事先信息或人类注释作为培训的额外输入。为了解决这一限制，我们提出了各种单词掩码（VMASK）方法，以自动学习特定于任务的重要词并减少有关分类的无关信息，这最终改善了模型预测的解释性。通过在七个基准文本分类数据集上使用三个神经文本分类器（CNN，LSTM和BERT）评估所提出的方法。实验显示了VMASK在提高模型预测准确性和解释性方面的有效性。
To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations. A new line of work on improving model interpretability has just started, and many existing methods require either prior information or human annotations as additional inputs in training. To address this limitation, we propose the variational word mask (VMASK) method to automatically learn task-specific important words and reduce irrelevant information on classification, which ultimately improves the interpretability of model predictions. The proposed method is evaluated with three neural text classifiers (CNN, LSTM, and BERT) on seven benchmark text classification datasets. Experiments show the effectiveness of VMASK in improving both model prediction accuracy and interpretability.
