  Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
Despite the strong performance, however, it lacks the nice interpretability as
in state space models. In this paper, we present a way to combine the best of
both worlds by introducing State Space LSTM (SSL) models that generalizes the
earlier work \cite{zaheer2017latent} of combining topic models with LSTM.
However, unlike \cite{zaheer2017latent}, we do not make any factorization
assumptions in our inference algorithm. We present an efficient sampler based
on sequential Monte Carlo (SMC) method that draws from the joint posterior
directly. Experimental results confirms the superiority and stability of this
SMC inference algorithm on a variety of domains.
