  We present Variational Aspect-based Latent Topic Allocation (VALTA), a family
of autoencoding topic models that learn aspect-based representations of
reviews. VALTA defines a user-item encoder that maps bag-of-words vectors for
combined reviews associated with each paired user and item onto structured
embeddings, which in turn define per-aspect topic weights. We model individual
reviews in a structured manner by inferring an aspect assignment for each
sentence in a given review, where the per-aspect topic weights obtained by the
user-item encoder serve to define a mixture over topics, conditioned on the
aspect. The result is an autoencoding neural topic model for reviews, which can
be trained in a fully unsupervised manner to learn topics that are structured
into aspects. Experimental evaluation on large number of datasets demonstrates
that aspects are interpretable, yield higher coherence scores than
non-structured autoencoding topic model variants, and can be utilized to
perform aspect-based comparison and genre discovery.
