Hierarchical Sequence-to-Sequence Model for Multi-Label Text Classification

We propose a novel sequence-to-sequence model for multi-label text classification, based on a “parallel encoding, serial decoding” strategy. The model combines a convolutional neural network and self-attention in parallel as the encoder to extract fine-grained local neighborhood information and global interaction information from the source text. We design a hierarchical decoder to decode and generate the label sequence. Our method not only gives full consideration to the interpretable fine-gained information in the source text but also effectively utilizes the information to generate the label sequence. We conducted a large number of comparative experiments on three datasets. The results show that the proposed model has significant advantages over the state-of-the-art baseline model. In addition, our analysis demonstrates that our model is competitive with the RNN-based Seq2Seq models and that it is more robust at handling datasets with a high label/sample ratio.