Discriminative Reranking for Machine Translation

This paper describes the application of discriminative reranking techniques to the problem of machine translation. For each sentence in the source language, we obtain from a baseline statistical machine translation system, a ranked best list of candidate translations in the target language. We introduce two novel perceptroninspired 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-theart performance in machine translation.

• Type: Conference
• Year: 2004
• Where: NAACL
• Authors: Libin Shen, Anoop Sarkar, Franz Josef Och
• Keywords: Baseline Statistical Machine, Discriminative Reranking Techniques, Machine Translation, NAACL 2004, NAACL 2007