We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations based on activation clusters, first derivative saliency, and embedding space transformations, helping us automatically identify several subtle linguistics markers of politeness theories. Further, this analysis reveals multiple novel, high-scoring politeness strategies which, when added back as new features, reduce the accuracy gap between the original featurized system and the neural model, thus providing a clear quantitative interpretation of the success of these neural networks.

通过简单的卷积神经网络和多种网络可视化技术实现了一种较好的有关礼貌性预测的可解释神经网络，从而自动识别礼貌性理论的多种难以捉摸的语言学特征，发现多种新的高得分礼貌策略，提高了网络预测准确性并提供了神经网络成功的定量解释。

解释神经网络以提高礼貌理解能力