  We introduce neural particle smoothing, a sequential Monte Carlo method for
sampling annotations of an input string from a given probability model. In
contrast to conventional particle filtering algorithms, we train a proposal
distribution that looks ahead to the end of the input string by means of a
right-to-left LSTM. We demonstrate that this innovation can improve the quality
of the sample. To motivate our formal choices, we explain how our neural model
and neural sampler can be viewed as low-dimensional but nonlinear
approximations to working with HMMs over very large state spaces.
