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Structure Compilation: Trading Structure for Features
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. Structured models often achieve excellent performance but can be slow at test time. We investigate structure compilation, where we replace structure with features, which are often computationally simpler but unfortunately statistically more complex. We analyze this tradeoff theoretically and empirically on three natural language processing tasks. We also introduce a simple method to transfer predictive power from structure to features via unlabeled data, while incurring a minimal statistical penalty.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
