Multi-label text classification aims to extract all the related labels from a sentence, which can be viewed as a sequence generation problem. However, the labels in training dataset are unordered. We propose to treat it as a direct set prediction problem and don't need to consider the order of labels. Besides, in order to model the correlation between labels, the adjacency matrix is constructed through the statistical relations between labels and GCN is employed to learn the label information. Based on the learned label information, the set prediction networks can both utilize the sentence information and label information for multi-label text classification simultaneously. Furthermore, the Bhattacharyya distance is imposed on the output probability distributions of the set prediction networks to increase the recall ability. Experimental results on four multi-label datasets show the effectiveness of the proposed method and it outperforms previous method a substantial margin.

翻译：多标签文本分类旨在从句子中提取所有相关标签，这可以视为一个序列生成问题。然而，训练数据集中的标签是无序的。我们提出将其直接作为集合预测问题来处理，无需考虑标签顺序。此外，为建模标签间的相关性，通过标签之间的统计关系构建邻接矩阵，并采用图卷积网络（GCN）学习标签信息。基于学习到的标签信息，集合预测网络能够同时利用句子信息和标签信息进行多标签文本分类。进一步地，将 Bhattacharyya 距离施加于集合预测网络的输出概率分布上，以提升召回能力。在四个多标签数据集上的实验结果表明，所提方法具有有效性，且相比先前方法取得了显著优势。