@article{vieira-eisner-2017-learning,
    title = "Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing",
    author = "Vieira, Tim  and
      Eisner, Jason",
    editor = "Lee, Lillian  and
      Johnson, Mark  and
      Toutanova, Kristina",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest_wac_2008/Q17-1019/",
    doi = "10.1162/tacl_a_00060",
    pages = "263--278",
    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."
}