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, allo...
