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

TACL, Volume 5, 2017, Pages 263-278.

Cited by: 6|Views16
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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 al...More

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