Neural Particle Smoothing for Sampling from Conditional Sequence Models
This addresses sampling efficiency in sequence modeling tasks, though it appears incremental as an enhancement to existing Monte Carlo methods.
The paper tackles the problem of sampling annotations from conditional sequence models by introducing neural particle smoothing, which uses a right-to-left LSTM to look ahead in the input string, improving sample quality compared to conventional particle filtering methods.
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.