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.- Anthology ID:
- Q17-1019
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 5
- Month:
- Year:
- 2017
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 263–278
- Language:
- URL:
- https://aclanthology.org/Q17-1019
- DOI:
- 10.1162/tacl_a_00060
- Cite (ACL):
- Tim Vieira and Jason Eisner. 2017. Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing. Transactions of the Association for Computational Linguistics, 5:263–278.
- Cite (Informal):
- Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing (Vieira & Eisner, TACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q17-1019.pdf
- Data
- Penn Treebank