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A Label-Specific Attention-Based Network with Regularized Loss for Multi-label Classification

机译：基于标签的基于注意力的，具有规则损失的多标签分类网络

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摘要

In a multi-label text classification task, different parts of a document do not contribute equally to predicting labels. Most existing approaches failed to consider this problem. Several methods have been proposed to take this problem into account. However, they just utilized hidden representations of neural networks as input of attention mechanism, not combining with label information. In this work, we propose an improved attention-based neural network model for multi-label text classification, which can obtain the weights of attention mechanism by computing the similarity between each label and each word of documents. This model adds the label information into text representations which can select the most informative words accurately for predicting labels. Besides, compared with single-label classification, the labels of multi-label classification may have some correlations such as co-occurrence or conditional probability relationship. So we also propose a special regular-ization term for this model, which can help to exploit label correlations by using label co-occurrence matrix. Experimental results on AAPD and RCV1-V2 datasets demonstrate that the proposed model yields a significant performance gain compared to many state-of-the-art approaches.
机译：在多标签文本分类任务中，文档的不同部分对预测标签的贡献不同。现有的大多数方法都没有考虑这个问题。已经提出了几种方法来考虑这个问题。但是，他们只是利用神经网络的隐藏表示作为注意机制的输入，而不是与标签信息结合。在这项工作中，我们提出了一种改进的基于注意力的神经网络模型，用于多标签文本分类，该模型可以通过计算每个标签与文档每个单词之间的相似度来获得注意力机制的权重。该模型将标签信息添加到文本表示中，该文本表示可以准确地选择信息最多的单词来预测标签。此外，与单标签分类相比，多标签分类的标签可能具有一定的相关性，例如共现或条件概率关系。因此，我们还为此模型提出了一个特殊的正则化术语，它可以通过使用标签共现矩阵来帮助利用标签相关性。在AAPD和RCV1-V2数据集上的实验结果表明，与许多最新方法相比，该模型可显着提高性能。

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