A Sequence Alignment Model Based on the Averaged Perceptron

We describe a discriminatively trained sequence alignment model based on the averaged perceptron. In common with other approaches to sequence modeling using perceptrons, 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 comparable 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 several problems. We show that the proposed model performs at least as well as an approach based on statistical machine translation on two problems of name transliteration, and provide evidence that the combination of the two approaches promises further improvement.