Discriminative Reranking for Machine Translation

Abstract

This paper describes the application of discrim-inative reranking techniques to the problem of machine translation. For each sentence in the source language, we obtain from a baseline sta-tistical machine translation system, a ranked -best list of candidate translations in the target language. We introduce two novel perceptron-inspired reranking algorithms that improve on the quality of machine translation over the baseline system based on evaluation using the BLEU metric. We provide experimental results on the NIST 2003 Chinese-English large data track evaluation. We also provide theoretical analysis of our algorithms and experiments that verify that our algorithms provide state-of-the-art performance in machine translation. 1