The aim in multi-label text classification is to assign a set of labels to a given document. Previous classifier-chain and sequence-to-sequence models have been shown to have a powerful ability to capture label correlations. However, they rely heavily on the label order, while labels in multi-label data are essentially an unordered set. The performance of these approaches is therefore highly variable depending on the order in which the labels are arranged. To avoid being dependent on label order, we design a reasoning-based algorithm named Multi-Label Reasoner (ML-Reasoner) for multi-label classification. ML-Reasoner employs a binary classifier to predict all labels simultaneously and applies a novel iterative reasoning mechanism to effectively utilize the inter-label information, where each instance of reasoning takes the previously predicted likelihoods for all labels as additional input. This approach is able to utilize information between labels, while avoiding the issue of label-order sensitivity. Extensive experiments demonstrate that our method outperforms state-of-the art approaches on the challenging AAPD dataset. We also apply our reasoning module to a variety of strong neural-based base models and show that it is able to boost performance significantly in each case.

多标签文本分类的目的是为给定文档分配一组标签。先前的分类器链和序列至序列模型已显示具有强大的捕获标签相关性的能力。但是，它们在很大程度上依赖于标签顺序，而多标签数据中的标签本质上是无序集合。因此，根据标签的排列顺序，这些方法的性能变化很大。为了避免依赖标签顺序，我们为多标签分类设计了一种基于推理的算法，称为多标签推理器（ML-Reasoner）。ML-Reasoner使用二进制分类器来同时预测所有标签，并应用新颖的迭代推理机制来有效利用标签间信息，所有标签作为附加输入。这种方法能够利用标签之间的信息，同时避免出现标签顺序敏感性问题。大量实验表明，在具有挑战性的AAPD数据集上，我们的方法优于最新方法。我们还将推理模块应用于各种基于神经的强大基础模型，并表明在每种情况下它都能显着提高性能。