Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high-dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. In solving these problems, we propose a novel end-to-end model called CRAFL, which is based on the convolutional layer with attention mechanism, recurrent neural networks, and improved focal loss function. First, the residual network (ResNet) extracts phrase semantic representations from word embedding vectors and reduces the dimensionality of the input matrix. Then, the attention mechanism differentiates the focus on the output of ResNet, and the long short-term memory layer learns the features of the sequences. Lastly but most significantly, we apply an improved focal loss function to mitigate the problem of data class imbalance. Our model is compared with other state-of-the-art models on the long discourse dataset, and CRAFL model has proven be more efficient for this task.

中文翻译：

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基于改进焦点损失神经网络的长话语文本语义分类


汉语长篇话语的语义分类是一项重要且具有挑战性的任务。话语文本是高维且稀疏的。此外，当数据集的类别数量较多时，数据分布会严重不平衡。为了解决这些问题，我们提出了一种名为 CRAFL 的新型端到端模型，该模型基于具有注意机制的卷积层、循环神经网络和改进的焦点损失函数。首先，残差网络（ResNet）从词嵌入向量中提取短语语义表示并降低输入矩阵的维度。然后，注意力机制将注意力集中在ResNet的输出上，长短期记忆层学习序列的特征。最后但最重要的是，我们应用改进的焦点损失函数来减轻数据类不平衡的问题。我们的模型在长话语数据集上与其他最先进的模型进行了比较，事实证明 CRAFL 模型对于这项任务更加有效。