  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.
