Discriminative Reranking for Machine Translation

@inproceedings{citeulike:391994, abstract = {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 n- 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.}, address = {Boston, USA}, author = {Shen, Libin and Sarkar, Anoop and Och, Franz }, booktitle = {Human Language Technology Conference and the 5th Meeting of the North American Association for Computational Linguistics: HLT-NAACL 2004}, citeulike-article-id = {391994}, keywords = {nlp}, month = {May}, posted-at = {2005-11-13 17:54:16}, priority = {0}, title = {Discriminative Reranking for Machine Translation}, year = {2004} }

TY - CONF ID - citeulike:391994 L3 - citeulike-article-id:391994 TI - Discriminative Reranking for Machine Translation T2 - Human Language Technology Conference and the 5th Meeting of the North American Association for Computational Linguistics: HLT-NAACL 2004 AD - Boston, USA N2 - 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 n- 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. KW - nlp AU - Shen, Libin AU - Sarkar, Anoop AU - Och, Franz PY - 2004/05// ER -