Title: Neural Models for Documents with Metadata,
Abstract: Most real-world document collections involve various types of metadata, such
as author, source, and date, and yet the most commonly-used approaches to
modeling text corpora ignore this information. While specialized models have
been developed for particular applications, few are widely used in practice, as
customization typically requires derivation of a custom inference algorithm. In
this paper, we build on recent advances in variational inference methods and
propose a general neural framework, based on topic models, to enable flexible
incorporation of metadata and allow for rapid exploration of alternative
models. Our approach achieves strong performance, with a manageable tradeoff
between perplexity, coherence, and sparsity. Finally, we demonstrate the
potential of our framework through an exploration of a corpus of articles about
US immigration.