- Discriminative syntactic reranking for statistical machine translation
- Ninth Conference of the Association for Machine Translation in the Americas (AMTA 2010), Denver, CO, USA
- Book/source title
- Ninth Conference of the Association for Machine Translation in the Americas (AMTA 2010), Denver, CO
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performance is evaluated for Arabic-to-English translation using NIST’s MT-Eval benchmarks. Whilst parse trees do not consistently help, we show how features extracted from a simple Part-of-Speech annotation layer outperform two competitive baselines, leading to significant BLEU improvements on three different test sets.
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.