Paper:
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 Zaheer et al. (2017) of combining topic models with LSTM. However, unlike Zaheer et al. (2017), 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.

Review:
This paper introduces a novel extension of the LSTM which incorporates stochastic inputs at each timestep. These stochastic inputs are themselves dependent on the LSTM state at the previous timestep. Considering the stochastic dependencies, this then yields a highly flexible non-Markov state space model, where the latent variable transitions are partially parameterized by an LSTM update.
Naturally, the challenges are then efficiently estimating parameters and performing inference over the latent states. Here, SMC (and conditional SMC / particle Gibbs) are used for inference over the latent states z. A particularly nice touch is that even when the LSTM model is used for the transitions in the latent space, so long as the conditional distributions p(z_t | z_{1:t-1}) are conjugate with the emission distribution then it is possible to compute the optimal forward filtering proposal distribution in closed form, as done for the conditionally Gaussian (with affine Gaussian observations) and conditionally multinomial models considered here. Note that this really is a special feature of the models under consideration, though: for example, if the emission distribution p(x_t | z_t) is instead a *nonlinear* Gaussian, then one would have to fall back to bootstrap proposals. This probably deserves some mention: equations (13) are not, generally, tractable to integrate or normalize.
I think this paper is missing a few necessary details on how the overall optimization algorithm proceeds, which I would like to see in an update. I understand that particle Gibbs updates (or SMC) are used to approximate the posterior distribution in a Monte Carlo EM algorithm. However, this does leave some questions:
1. For the M step, how are the \omega parameters (of the LSTM) handled in equation (8)? I understand that due to the particular models considered, maximum likelihood estimates of \phi can be found in closed form. However, that’s not the case for \omega. Is a gradient descent algorithm run to convergence? Or is a single gradient step taken, interleaved with a single PG update? Or something else?
2. How reliably does the algorithm as a whole converge? Monte Carlo EM does not in general have convergence guarantees of “standard” EM (i.e. each step is not guaranteed to monotonically improve the lower bound). This might be fine! But, I think requires a bit of discussion.
3. Is it necessary to include a replenishing operation (or independent MCMC steps) in the particle Gibbs algorithm? A known issue when running an iterated conditional SMC algorithm like this is that path degeneracy can make it very difficult for the PG kernel to mix well over the early time steps in the LSTM. Does this issue appear here? How many particles P are needed to efficiently mix, when considering time series of length T?