Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing
TACL, Volume 5, 2017, Pages 263-278.
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 al...More
PPT (Upload PPT)