Paper: Sequential Conditional Generalized Iterative Scaling

ACL ID P02-1002
Title Sequential Conditional Generalized Iterative Scaling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

We describe a speedup for training conditional maxi- mum entropy models. The algorithm is a simple vari- ation on Generalized Iterative Scaling, but converges roughly an order of magnitude faster, depending on the number of constraints, and the way speed is mea- sured. Rather than attempting to train all model pa- rameters simultaneously, the algorithm trains them sequentially. The algorithm is easy to implement, typically uses only slightly more memory, and will lead to improvements for most maximum entropy problems.