  Distributed dense word vectors have been shown to be effective at capturing
token-level semantic and syntactic regularities in language, while topic models
can form interpretable representations over documents. In this work, we
describe lda2vec, a model that learns dense word vectors jointly with
Dirichlet-distributed latent document-level mixtures of topic vectors. In
contrast to continuous dense document representations, this formulation
produces sparse, interpretable document mixtures through a non-negative simplex
constraint. Our method is simple to incorporate into existing automatic
differentiation frameworks and allows for unsupervised document representations
geared for use by scientists while simultaneously learning word vectors and the
linear relationships between them.
