@article{TACL924,
	author = {Vieira, Tim  and  Eisner, Jason},
	title = {Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {5},
	year = {2017},
	keywords = {},
	abstract = {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.},
	issn = {2307-387X},
	url = {https://transacl.org/ojs/index.php/tacl/article/view/924},
	pages = {263--278}
}