Neural Particle Smoothing for Sampling from Conditional Sequence Models
north american chapter of the association for computational linguistics, pp. 929-941, 2018.
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 tha...More
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