A novel reasoning mechanism for multi-label text classification

Highlights：

• A novel reasoning-based algorithm named ML-Reasoner for multi-label text classification is proposed.

• Each instance of reasoning in this method takes the previously predicted likelihoods for all labels as additional input.

• Not only is the method able to avoid the dependency of label orders completely, but it also achieves competitive performance when handling with multi-label datasets.

• Applying the reasoning mechanism to three strong neural-based base models can achieve significant performance improvements on all two data sets.

• The method achieves state-of-the-art results on two challenging multi-label datasets.