Multi-label document classification, associating one document instance with a set of relevant labels, is attracting more and more research attention.Existing methods explore the incorporation of information beyond text, such as document metadata or label structure.These approaches however either simply utilize the semantic information of metadata or employ the predefined parent-child label hierarchy, ignoring the heterogeneous graphical structures of metadata and labels, which we believe are crucial for accurate multi-label document classification.Therefore, in this paper, we propose a novel neural network based approach for multi-label document classification, in which two heterogeneous graphs are constructed and learned using heterogeneous graph transformers.One is metadata heterogeneous graph, which models various types of metadata and their topological relations.The other is label heterogeneous graph, which is constructed based on both the labels' hierarchy and their statistical dependencies.Experimental results on two benchmark datasets show the proposed approach outperforms several stateof-the-art baselines.

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).However, these models either typically assume improper prior (e.g.Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document.To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling.The proposed BAT builds a twoway projection between the document-topic distribution and the document-word distribution.It uses a generator to capture the semantic patterns from texts and an encoder for topic inference.Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT.To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments.The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines.Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy.