Paper: Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models?

ACL ID W11-0328
Title Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models?
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2011
Authors

This paper shows that the performance of history-based models can be significantly im- proved by performing lookahead in the state space when making each classification deci- sion. Instead of simply using the best ac- tion output by the classifier, we determine the best action by looking into possible se- quences of future actions and evaluating the final states realized by those action sequences. We present a perceptron-based parameter op- timization method for this learning frame- work and show its convergence properties. The proposed framework is evaluated on part- of-speech tagging, chunking, named entity recognition and dependency parsing, using standard data sets and features. Experimental results demonstrate that history-based models with lookahead are as competitive as globally opt...