Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing.

Pruning hypotheses during dynamic programming is commonly used to speed up inference in settings such as parsing.  Unlike prior work, we train a pruning policy under an objective that measures end-to-end performance: we search for a fast and accurate policy. This poses a difficult machine learning problem, which we tackle with the LOLS algorithm.  LOLS training must continually compute the effects of changing pruning decisions: we show how to make this efficient in the constituency parsing setting, via dynamic programming and change propagation algorithms.  We find that optimizing end-to-end performance in this way leads to a better Pareto frontier---i.e., parsers which are more accurate for a given runtime.