Paper: A Sequence Alignment Model Based on the Averaged Perceptron

ACL ID D07-1025
Title A Sequence Alignment Model Based on the Averaged Perceptron
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We describe a discriminatively trained se- quence alignment model based on the av- eraged perceptron. In common with other approaches to sequence modeling using per- ceptrons, and in contrast with comparable generative models, this model permits and transparently exploits arbitrary features of input strings. The simplicity of perceptron training lends more versatility than compa- rable approaches, allowing the model to be applied to a variety of problem types for which a learned edit model might be useful. We enumerate some of these problem types, describe a training procedure for each, and evaluate the model’s performance on sev- eral problems. We show that the proposed model performs at least as well as an ap- proach based on statistical machine transla- tion on two problems of name tr...