Multi-Label Text Classification (MLTC) aims to assign the most relevant labels to each given text. Existing methods demonstrate that label dependency can help to improve the model's performance. However, the introduction of label dependency may cause the model to suffer from unwanted prediction bias. In this study, we attribute the bias to the model's misuse of label dependency, i.e., the model tends to utilize the correlation shortcut in label dependency rather than fusing text information and label dependency for prediction. Motivated by causal inference, we propose a CounterFactual Text Classifier (CFTC) to eliminate the correlation bias, and make causality-based predictions. Specifically, our CFTC first adopts the predict-then-modify backbone to extract precise label information embedded in label dependency, then blocks the correlation shortcut through the counterfactual de-bias technique with the help of the human causal graph. Experimental results on three datasets demonstrate that our CFTC significantly outperforms the baselines and effectively eliminates the correlation bias in datasets.

翻译：多标签文本分类旨在为每个给定文本分配最相关的标签。现有方法表明标签依赖关系有助于提升模型性能。然而，引入标签依赖关系可能导致模型产生不必要的预测偏差。本研究将偏差归因于模型对标签依赖关系的误用，即模型倾向于利用标签依赖中的相关性捷径，而非融合文本信息与标签依赖进行预测。受因果推断启发，我们提出反事实文本分类器以消除相关性偏差，并实现基于因果关系的预测。具体而言，我们的CFTC首先采用"先预测后修正"的主干结构提取标签依赖中蕴含的精确标签信息，随后借助人工因果图，通过反事实去偏技术阻断相关性捷径。三个数据集上的实验结果表明，所提CFTC显著优于基线模型，并有效消除了数据集中的相关性偏差。