  Multi-label text classification refers to the problem of assigning each given
document its most relevant labels from the label set. Commonly, the metadata of
the given documents and the hierarchy of the labels are available in real-world
applications. However, most existing studies focus on only modeling the text
information, with a few attempts to utilize either metadata or hierarchy
signals, but not both of them. In this paper, we bridge the gap by formalizing
the problem of metadata-aware text classification in a large label hierarchy
(e.g., with tens of thousands of labels). To address this problem, we present
the MATCH solution -- an end-to-end framework that leverages both metadata and
hierarchy information. To incorporate metadata, we pre-train the embeddings of
text and metadata in the same space and also leverage the fully-connected
attentions to capture the interrelations between them. To leverage the label
hierarchy, we propose different ways to regularize the parameters and output
probability of each child label by its parents. Extensive experiments on two
massive text datasets with large-scale label hierarchies demonstrate the
effectiveness of MATCH over state-of-the-art deep learning baselines.
