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.
IEEE Access