As an important branch of Nature Language Processing (NLP), how to extract useful text information and effective long-range associations has always been a bottleneck for text classification. With the great effort of deep learning researchers, deep Convolutional Neural Networks (CNNs) have made remarkable achievements in Computer Vision but still controversial in NLP tasks. In this paper, we propose a novel deep CNN named Deep Pyramid Temporal Convolutional Network (DPTCN) for short text classification, which is mainly consisting of concatenated embedding layer, causal convolution, 1/2 max pooling down-sampling and residual blocks. It is worth mentioning that our work was highly inspired by two well-designed models: one is temporal convolutional network for sequential modeling; another is deep pyramid CNN for text categorization; as their applicability and pertinence remind us how to build a model in a special domain. In the experiments, we evaluate the proposed model on 7 datasets with 6 models and analyze the impact of three different embedding methods. The results prove that our work is a good attempt to apply word-level deep convolutional network in short text classification.

中文翻译：

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DPTCN：一种用于短文本分类的新型深度 CNN 模型

作为自然语言处理（NLP）的一个重要分支，如何提取有用的文本信息和有效的长程关联一直是文本分类的瓶颈。在深度学习研究人员的大力努力下，深度卷积神经网络（CNN）在计算机视觉方面取得了显著成就，但在 NLP 任务中仍存在争议。在本文中，我们提出了一种用于短文本分类的新型深度 CNN，名为 Deep Pyramid Temporal Convolutional Network (DPTCN)，主要由级联嵌入层、因果卷积、1/2 最大池化下采样和残差块组成。值得一提的是，我们的工作受到了两个精心设计的模型的高度启发：一个是用于顺序建模的时间卷积网络；另一个是用于文本分类的深金字塔CNN；因为它们的适用性和针对性提醒我们如何在特殊领域建立模型。在实验中，我们在 6 个模型的 7 个数据集上评估了所提出的模型，并分析了三种不同嵌入方法的影响。结果证明我们的工作是将词级深度卷积网络应用于短文本分类的一个很好的尝试。