  Mixture models trained via EM are among the simplest, most widely used and
well understood latent variable models in the machine learning literature.
Surprisingly, these models have been hardly explored in text generation
applications such as machine translation. In principle, they provide a latent
variable to control generation and produce a diverse set of hypotheses. In
practice, however, mixture models are prone to degeneracies---often only one
component gets trained or the latent variable is simply ignored. We find that
disabling dropout noise in responsibility computation is critical to successful
training. In addition, the design choices of parameterization, prior
distribution, hard versus soft EM and online versus offline assignment can
dramatically affect model performance. We develop an evaluation protocol to
assess both quality and diversity of generations against multiple references,
and provide an extensive empirical study of several mixture model variants. Our
analysis shows that certain types of mixture models are more robust and offer
the best trade-off between translation quality and diversity compared to
variational models and diverse decoding approaches.\footnote{Code to reproduce
the results in this paper is available at
\url{https://github.com/pytorch/fairseq}}
